Integrative analysis of the melanoma transcriptome

Genome-wide methylated CpG island profiles of melanoma cells reveal a melanoma coregulation network

Metastatic melanoma is a malignant cancer with generally poor prognosis, with no targeted chemotherapy. To identify epigenetic changes related to melanoma, we have determined genome-wide methylated CpG island distributions by next-generation sequencing. Melanoma chromosomes tend to be differentially methylated over short CpG island tracts. CpG islands in the upstream regulatory regions of many coding and noncoding RNA genes, including, for example, TERC, which encodes the telomerase RNA, exhibit extensive hypermethylation, whereas several repeated elements, such as LINE 2, and several LTR elements, are hypomethylated in advanced stage melanoma cell lines. By using CpG island demethylation profiles, and by integrating these data with RNA-seq data obtained from melanoma cells, we have identified a co-expression network of differentially methylated genes with significance for cancer related functions. Focused assays of melanoma patient tissue samples for CpG island methylation near the noncoding RNA gene SNORD-10 demonstrated high specificity.

Genomic organization of human transcription initiation complexes

The human genome is pervasively transcribed, yet only a small fraction is coding. Here we address whether this noncoding transcription arises at promoters, and detail the interactions of initiation factors TBP, TFIIB, and RNA polymerase (Pol) II. Using ChIP-exo, we identify ~160,000 transcription initiation complexes across the human K562 genome, and more in other cancer genomes. Only ~5% associate with mRNA genes. The remaining associate with non-polyadenylated noncoding transcription. Regardless, Pol II moves into a transcriptionally paused state, and TBP/TFIIB remain at the promoter. Remarkably, the vast majority of locations contain the four core promoter elements: BRE_u, TATA, BRE_d, and INR, in constrained positions. All but the INR also reside at Pol III promoters, where TBP makes similar contacts. This comprehensive and high resolution genome-wide detection of the initiation machinery produces a consolidated view of transcription initiation events from yeast to humans at Pol II/III TATA-containing/TATA-less coding and noncoding genes.

Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: applying RNA-sequencing analysis on archived specimens

OBJECTIVES

To identify gene signatures in transitional cell carcinoma that can differentiate high-grade T1 nonprogressive (T1NP) bladder cancer (BCa) from those T1 progressive (T1P) tumors that progress to muscularis propria-invasive T2 tumors.

MATERIALS AND METHODS

We performed a high-throughput RNA sequencing (RNA-Seq) on formalin-fixed and paraffin-embedded BCa specimens with clinical pathologic characteristics best representing the general clinical development of the disease. For the T1NP group, only patients with long-term follow-up (6-17y) and periodic examinations (average of 4 resections and 9 cytology tests) were selected. For the T1P group, only patients in whom a complete resection was performed after a minimum of 8 months after the initial T1 diagnosis were selected, therefore eliminating the possibility of underdiagnosis. Only samples in which muscularis propria was present and uninvolved were included, further assuring a correct diagnosis. The RNA-Seq reads were mapped to the human genome build NCBI 36 (hg18) using TopHat with no mismatch. After alignment to the transcriptome and expression quantification, a linear statistical model was built using Limma between T1NP and T1P samples to identify differentially expressed genes.

RESULTS

Overall, 5,561 genes were mapped to all samples and used for RNA-Seq analysis to identify a gene signature that was significantly and differentially expressed between patients with T1NP BCa and patients with T1P BCa. Signature-based stratification indicated the gene signature correlated notably with the time of T1 development to T2 tumor, suggesting that the molecular signature might be used as an independent predictor for the pace of high-grade T1 BCa progression.

CONCLUSIONS

This is the first demonstration that RNA-Seq can be applied as a powerful tool to study BCa using formalin-fixed and paraffin-embedded specimens. We identified a gene signature that can distinguish patients diagnosed with high-grade T1 BCas that remain as non-muscle invasive tumors from those patients with cancers progressing to muscle-invasive tumors. Our findings will make future large-scale clinical cohort studies and clinical trial-based studies possible and help the development of prognostic tools for accurate prediction of T1 BCa progression that may considerably influence the clinical decision-making process, treatment regimen, and patient survival.

Prostate cancer invasion and metastasis: insights from mining genomic data

Prostate cancer (PCa) is the second most commonly diagnosed malignancy in men in the Western world and the second leading cause of cancer-related deaths among men worldwide. Although most cancers have the potential to metastasize under appropriate conditions, PCa favors the skeleton as a primary site of metastasis, suggesting that the bone microenvironment is conducive to its growth. PCa metastasis proceeds through a complex series of molecular events that include angiogenesis at the site of the original tumor, local migration within the primary site, intravasation into the blood stream, survival within the circulation, extravasation of the tumor cells to the target organ and colonization of those cells within the new site. In turn, each one of these steps involves a complicated chain of events that utilize multiple protein-protein interactions, protein signaling cascades and transcriptional changes. Despite the urgent need to improve current biomarkers for diagnosis, prognosis and drug resistance, advances have been slow. Global gene expression methods such as gene microarrays and RNA sequencing enable the study of thousands of genes simultaneously and allow scientists to examine molecular pathways of cancer pathogenesis. In this review, we summarize the current literature that explored high-throughput transcriptome analysis toward the advancement of biomarker discovery for PCa. Novel biomarkers are strongly needed to enable more accurate detection of PCa, improve prediction of tumor aggressiveness and facilitate the discovery of new therapeutic targets for tailored medicine. Promising molecular markers identified from gene expression profiling studies include HPN, CLU1, WT1, WNT5A, AURKA and SPARC.

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

A novel approach for characterizing microsatellite instability in cancer cells

Microsatellite instability (MSI) is characterized by the expansion or contraction of DNA repeat tracts as a consequence of DNA mismatch repair deficiency (MMRD). Accurate detection of MSI in cancer cells is important since MSI is associated with several cancer subtypes and can help inform therapeutic decisions. Although experimental assays have been developed to detect MSI, they typically depend on a small number of known microsatellite loci or mismatch repair genes and have limited reliability. Here, we report a novel genome-wide approach for MSI detection based on the global detection of insertions and deletions (indels) in microsatellites found in expressed genes. Our large-scale analyses of 20 cancer cell lines and 123 normal individuals revealed striking indel features associated with MSI: there is a significant increase of short microsatellite deletions in MSI samples compared to microsatellite stable (MSS) ones, suggesting a mechanistic bias of repair efficiency between insertions and deletions in normal human cells. By incorporating this observation into our MSI scoring metric, we show that our approach can correctly distinguish between MSI and MSS cancer cell lines. Moreover, when we applied this approach to primal tumor samples, our metric is also well consistent with diagnosed MSI status. Thus, our study offers new insight into DNA mismatch repair system, and also provides a novel MSI diagnosis method for clinical oncology with better reliability.

Breakpoint analysis of transcriptional and genomic profiles uncovers novel gene fusions spanning multiple human cancer types

Gene fusions, like BCR/ABL1 in chronic myelogenous leukemia, have long been recognized in hematologic and mesenchymal malignancies. The recent finding of gene fusions in prostate and lung cancers has motivated the search for pathogenic gene fusions in other malignancies. Here, we developed a “breakpoint analysis” pipeline to discover candidate gene fusions by tell-tale transcript level or genomic DNA copy number transitions occurring within genes. Mining data from 974 diverse cancer samples, we identified 198 candidate fusions involving annotated cancer genes. From these, we validated and further characterized novel gene fusions involving ROS1 tyrosine kinase in angiosarcoma (CEP85L/ROS1), SLC1A2 glutamate transporter in colon cancer (APIP/SLC1A2), RAF1 kinase in pancreatic cancer (ATG7/RAF1) and anaplastic astrocytoma (BCL6/RAF1), EWSR1 in melanoma (EWSR1/CREM), CDK6 kinase in T-cell acute lymphoblastic leukemia (FAM133B/CDK6), and CLTC in breast cancer (CLTC/VMP1). Notably, while these fusions involved known cancer genes, all occurred with novel fusion partners and in previously unreported cancer types. Moreover, several constituted druggable targets (including kinases), with therapeutic implications for their respective malignancies. Lastly, breakpoint analysis identified new cell line models for known rearrangements, including EGFRvIII and FIP1L1/PDGFRA. Taken together, we provide a robust approach for gene fusion discovery, and our results highlight a more widespread role of fusion genes in cancer pathogenesis.

Gene fusions represent an important class of cancer genes, created by rearrangements of the genome that bring together two different genes. Because they are unique to cancer cells, gene fusions are ideal diagnostic markers and therapeutic targets. While gene fusions were once thought restricted mainly to blood cancers, recent discoveries suggest they are more widespread. Here, we have developed an approach for mining DNA microarray data to detect the tell-tale signatures of gene fusions, as “breakpoints” occurring within the encoding DNA or expressed transcripts. We apply this approach to a large collection of nearly 1,000 human cancer specimens. From this analysis, we discover and verify twelve new gene fusions occurring in diverse cancer types. We verify that some of these rearrangements recur in other samples of the same cancer type (supporting a causal role) and that the cancers show dependency on the fusion for cancer cell growth. Notably, some of these fusions (e.g. CEP85L/ROS1 in angiosarcoma) represent the first for that cancer type and thus provide important new biological insight. Some are also good drug targets (including rearrangements of ROS1, RAF1, and CDK6 kinases), with clear implications for therapy.

microRNAs and ceRNAs: RNA networks in pathogenesis of cancer

microRNAs (miRNAs) are a class of endogenous, single-stranded non-coding RNAs of 20-23 nucleotides in length, functioning as negative regulators of gene expression at the post-transcriptional level. The dysregulation of miRNAs has been demonstrated to play critical roles in tumorigenesis, either through inhibiting tumor suppressor genes or activating oncogenes inappropriately. Besides their promising clinical applications in cancer diagnosis and treatment, recent studies have uncovered that miRNAs could act as a regulatory language, through which messenger RNAs, transcribed pseudogenes, and long noncoding RNAs crosstalk with each other and form a novel regulatory network. RNA transcripts involved in this network have been termed as competing endogenous RNAs (ceRNAs), since they influence each other's level by competing for the same pool of miRNAs through miRNA response elements (MREs) on their target transcripts. The discovery of miRNA-ceRNA network not only provides the possibility of an additional level of post-transcriptional regulation, but also dictates a reassessment of the existing regulatory pathways involved in cancer initiation and progression.

microRNAs and ceRNAs: RNA networks in pathogenesis of cancer

microRNAs (miRNAs) are a class of endogenous, single-stranded non-coding RNAs of 20-23 nucleotides in length, functioning as negative regulators of gene expression at the post-transcriptional level. The dysregulation of miRNAs has been demonstrated to play critical roles in tumorigenesis, either through inhibiting tumor suppressor genes or activating oncogenes inappropriately. Besides their promising clinical applications in cancer diagnosis and treatment, recent studies have uncovered that miRNAs could act as a regulatory language, through which messenger RNAs, transcribed pseudogenes, and long noncoding RNAs crosstalk with each other and form a novel regulatory network. RNA transcripts involved in this network have been termed as competing endogenous RNAs (ceRNAs), since they influence each other's level by competing for the same pool of miRNAs through miRNA response elements (MREs) on their target transcripts. The discovery of miRNA-ceRNA network not only provides the possibility of an additional level of post-transcriptional regulation, but also dictates a reassessment of the existing regulatory pathways involved in cancer initiation and progression.

Isoform level expression profiles provide better cancer signatures than gene level expression profiles

BACKGROUND

The majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. We hypothesized that isoform level expression profiles would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level.

METHODS

We analyzed 160 Affymetrix exon-array datasets, comprising cell lines of non-oncogenic or oncogenic tissue origins. We obtained the transcript-level and gene level expression estimates, and used unsupervised and supervised clustering algorithms to study the profile similarity between the samples at both gene and isoform levels.

RESULTS

Hierarchical clustering, based on isoform level expressions, effectively grouped the non-oncogenic and oncogenic cell lines with a virtually perfect homogeneity-grouping rate (97.5%), regardless of the tissue origin of the cell lines. However, gene levelthis rate was much lower, being 75% at best based on the gene level expressions. Statistical analyses of the difference between cancer and non-oncogenic samples identified the existence of numerous genes with differentially expressed isoforms, which otherwise were not significant at the gene level. We also found that canonical pathways of protein ubiquitination, purine metabolism, and breast-cancer regulation by stathmin1 were significantly enriched among genes thatshow differential expression at isoform level but not at gene level.

CONCLUSIONS

In summary, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. This study suggests the existence of an isoform signature, rather than a gene signature, which could be used to distinguish cancer cells from normal cells.

Existing and emerging technologies for tumor genomic profiling

Ongoing global genome characterization efforts are revolutionizing our knowledge of cancer genomics and tumor biology. In parallel, information gleaned from these studies on driver cancer gene alterations--mutations, copy number alterations, translocations, and/or chromosomal rearrangements--an be leveraged, in principle, to develop a cohesive framework for individualized cancer treatment. These possibilities have been enabled, to a large degree, by revolutionary advances in genomic technologies that facilitate systematic profiling for hallmark cancer genetic alterations at increasingly fine resolutions. Ongoing innovations in existing genomics technologies, as well as the many emerging technologies, will likely continue to advance translational cancer genomics and precision cancer medicine.

There is a world beyond protein mutations: the role of non-coding RNAs in melanomagenesis

Until recently, the general perception has been that mutations in protein-coding genes are responsible for tumorigenesis. With the discovery of (V600E)BRAF in about 50% of cutaneous melanomas, there was an increased effort to find additional mutations. However, mutations characterized in melanoma to date cannot account for the development of all melanomas. With the discovery of microRNAs as important players in melanomagenesis, protein mutations are no longer considered the sole drivers of tumors. Recent research findings have expanded the view for tumor initiation and progression to additional non-coding RNAs. The data suggest that tumorigenesis is likely an interplay between mutated proteins and deregulation of non-coding RNAs in the cell with an additional role of the tumor environment. With the exception of microRNAs, our knowledge of the role of non-coding RNAs in melanoma is in its infancy. Using few examples, we will summarize some of the roles of non-coding RNAs in tumorigenesis. Thus, there is a whole world beyond protein-coding sequences and microRNAs, which can cause melanoma.

Clinical integration of next-generation sequencing technology

Recent advances in next-generation sequencing (NGS) methods and technology have substantially reduced costs and operational complexity leading to production of benchtop sequencers and commercial software solutions for implementation in small research and clinical laboratories. This article addresses requirements and limitations to successful implementation of these systems, including (1) calibration and validation of the instrumentation, experimental paradigm, and primary readout, (2) secure data transfer, storage, and secondary processing, (3) implementation of software tools for targeted analysis, and (4) training of research and clinical personnel to evaluate data fidelity and interpret the molecular significance of the genomic output.

State-of-the-art fusion-finder algorithms sensitivity and specificity

Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way. Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions. Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data

We have developed a new method, SOAPfuse, to identify fusion transcripts from paired-end RNA-Seq data. SOAPfuse applies an improved partial exhaustion algorithm to construct a library of fusion junction sequences, which can be used to efficiently identify fusion events, and employs a series of filters to nominate high-confidence fusion transcripts. Compared with other released tools, SOAPfuse achieves higher detection efficiency and consumed less computing resources. We applied SOAPfuse to RNA-Seq data from two bladder cancer cell lines, and confirmed 15 fusion transcripts, including several novel events common to both cell lines. SOAPfuse is available at http://soap.genomics.org.cn/soapfuse.html.

Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

In the last decade, genome-wide transcriptome analyses have been routinely used to monitor tissue-, disease- and cell type-specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a novel and robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which significantly enhances detailed analyses of alternative transcript isoforms and identification of SNPs. We have determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including new candidate biomarkers for melanoma circulating tumor cells. Importantly, our protocol can easily be utilized for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

Revisiting global gene expression analysis

Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease, and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms.

High-throughput sequencing for biology and medicine

Advances in genome sequencing have progressed at a rapid pace, with increased throughput accompanied by plunging costs. But these advances go far beyond faster and cheaper. High-throughput sequencing technologies are now routinely being applied to a wide range of important topics in biology and medicine, often allowing researchers to address important biological questions that were not possible before. In this review, we discuss these innovative new approaches-including ever finer analyses of transcriptome dynamics, genome structure and genomic variation-and provide an overview of the new insights into complex biological systems catalyzed by these technologies. We also assess the impact of genotyping, genome sequencing and personal omics profiling on medical applications, including diagnosis and disease monitoring. Finally, we review recent developments in single-cell sequencing, and conclude with a discussion of possible future advances and obstacles for sequencing in biology and health.

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

High-throughput sequencing of the melanoma genome

Next-generation sequencing technologies are now common for whole-genome, whole-exome and whole-transcriptome sequencing (RNA-seq) of tumors to identify point mutations, structural or copy number alterations and changes in gene expression. A substantial number of studies have already been performed for melanoma. One study analysed eight melanoma cell lines with RNA-Seq technology and identified 11 novel melanoma gene fusions. Whole-exome sequencing of seven melanoma cell lines identified overlapping gain of function mutations in MAP2K1 (MEK1) and MAP2K2 (MEK2) genes. Integrative sequencing of cutaneous melanoma metastases using different sequencing platforms revealed a new somatic point mutation in HRAS and a structural rearrangement affecting CDKN2C (a CDK4 inhibitor). These latter sequencing-based discoveries may be used to motivate the inclusion of the affected patients into clinical trials with specific signalling pathway inhibitors. Taken together, we are at the beginning of an era with new sequencing technologies providing a more comprehensive view of cancer mutational landscapes and hereby a better understanding of their pathogenesis. This will also open interesting perspectives for new treatment approaches and clinical trial designs.

Next-generation sequencing in the clinic: promises and challenges

The advent of next generation sequencing (NGS) technologies has revolutionized the field of genomics, enabling fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy. Over the past years, rapid technological advances led by academic institutions and companies have continued to broaden NGS applications from research to the clinic. A recent crop of discoveries have highlighted the medical impact of NGS technologies on Mendelian and complex diseases, particularly cancer. However, the ever-increasing pace of NGS adoption presents enormous challenges in terms of data processing, storage, management and interpretation as well as sequencing quality control, which hinder the translation from sequence data into clinical practice. In this review, we first summarize the technical characteristics and performance of current NGS platforms. We further highlight advances in the applications of NGS technologies towards the development of clinical diagnostics and therapeutics. Common issues in NGS workflows are also discussed to guide the selection of NGS platforms and pipelines for specific research purposes.

The DEAD/DEAH box helicase, DDX11, is essential for the survival of advanced melanomas

Background

Despite continuous efforts to identify genes that are pivotal regulators of advanced melanoma and closely related to it, to determine which of these genes have to be blocked in their function to keep this highly aggressive disease in check, it is far from clear which molecular pathway(s) and specific genes therein, is the Achilles’ heel of primary and metastatic melanoma. In this report, we present data, which document that the DEAD-box helicase DDX11, which is required for sister chromatid cohesion, is a crucial gatekeeper for melanoma cell survival.

Methods

Performing immunohistochemistry and immunoblot analysis, we determined expression of DDX11 in melanoma tissues and cell lines. Following transfection of melanoma cells with a DDX11-specific siRNA, we conducted a qPCR analysis to determine downregulation of DDX11 in the transfected melanoma cells. In subsequent studies, which focused upon an analysis of fluorescently labeled as well as Giesma-stained chromosome spreads, a proliferation analysis and apoptosis assays, we determined the impact of suppressing DDX11 expression on melanoma cells representing advanced melanoma.

Result

The findings of the study presented herein document that DDX11 is upregulated with progression from noninvasive to invasive melanoma, and that it is expressed at high levels in advanced melanoma. Furthermore, and equally important, we demonstrate that blocking the expression of DDX11 leads not only to inhibition of melanoma cell proliferation and severe defects in chromosome segregation, but also drives melanoma cells rapidly into massive apoptosis.

Conclusion

To date, little is known as to whether helicases play a role in melanoma development and specifically, in the progression from early to advanced melanoma. In this report, we show that the helicase DDX11 is expressed at high levels in primary and metastatic melanoma, and that interfering with its expression leads to severe chromosome segregation defects, telomere shortening, and massive melanoma cell apoptosis. These findings suggest that DDX11 could be an important candidate for molecular targeted therapy for advanced melanoma.

Next generation sequencing in clinical medicine: Challenges and lessons for pathology and biomedical informatics

The Human Genome Project (HGP) provided the initial draft of mankind's DNA sequence in 2001. The HGP was produced by 23 collaborating laboratories using Sanger sequencing of mapped regions as well as shotgun sequencing techniques in a process that occupied 13 years at a cost of ~$3 billion. Today, Next Generation Sequencing (NGS) techniques represent the next phase in the evolution of DNA sequencing technology at dramatically reduced cost compared to traditional Sanger sequencing. A single laboratory today can sequence the entire human genome in a few days for a few thousand dollars in reagents and staff time. Routine whole exome or even whole genome sequencing of clinical patients is well within the realm of affordability for many academic institutions across the country. This paper reviews current sequencing technology methods and upcoming advancements in sequencing technology as well as challenges associated with data generation, data manipulation and data storage. Implementation of routine NGS data in cancer genomics is discussed along with potential pitfalls in the interpretation of the NGS data. The overarching importance of bioinformatics in the clinical implementation of NGS is emphasized.[7] We also review the issue of physician education which also is an important consideration for the successful implementation of NGS in the clinical workplace. NGS technologies represent a golden opportunity for the next generation of pathologists to be at the leading edge of the personalized medicine approaches coming our way. Often under-emphasized issues of data access and control as well as potential ethical implications of whole genome NGS sequencing are also discussed. Despite some challenges, it's hard not to be optimistic about the future of personalized genome sequencing and its potential impact on patient care and the advancement of knowledge of human biology and disease in the near future.

Reanalysis of RNA-sequencing data reveals several additional fusion genes with multiple isoforms

RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.

Revisiting global gene expression analysis

Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease, and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms.

TGF-β signaling, activated stromal fibroblasts, and cysteine cathepsins B and L drive the invasive growth of human melanoma cells

Accumulating evidence indicates that interactions between cancer cells and stromal cells are important for the development/progression of many cancers. Herein, we found that the invasive growth of melanoma cells in three-dimensional-Matrigel/collagen-I matrices is dramatically increased on their co-culture with embryonic or adult skin fibroblasts. Studies with fluorescent-labeled cells revealed that the melanoma cells first activate the fibroblasts, which then take the lead in invasion. To identify the physiologically relevant invasion-related proteases involved, we performed genome-wide microarray analyses of invasive human melanomas and benign nevi; we found up-regulation of cysteine cathepsins B and L, matrix metalloproteinase (MMP)-1 and -9, and urokinase- and tissue-type plasminogen activators. The mRNA levels of cathepsins B/L and plasminogen activators, but not MMPs, correlated with metastasis. The invasiveness/growth of the melanoma cells with fibroblasts was inhibited by cell membrane-permeable inhibitors of cathepsins B/L, but not by wide-spectrum inhibitors of MMPs. The IHC analysis of primary melanomas and benign nevi revealed cathepsin B to be predominantly expressed by melanoma cells and cathepsin L to be predominantly expressed by the tumor-associated fibroblasts surrounding the invading melanoma cells. Finally, cathepsin B regulated TGF-β production/signaling, which was required for the activation of fibroblasts and their promotion of the invasive growth of melanoma cells. These data provide a basis for testing inhibitors of TGF-β signaling and cathepsins B/L in the therapy of invasive/metastatic melanomas.

A landscape of driver mutations in melanoma

Despite recent insights into melanoma genetics, systematic surveys for driver mutations are challenged by an abundance of passenger mutations caused by carcinogenic UV light exposure. We developed a permutation-based framework to address this challenge, employing mutation data from intronic sequences to control for passenger mutational load on a per gene basis. Analysis of large-scale melanoma exome data by this approach discovered six novel melanoma genes (PPP6C, RAC1, SNX31, TACC1, STK19, and ARID2), three of which-RAC1, PPP6C, and STK19-harbored recurrent and potentially targetable mutations. Integration with chromosomal copy number data contextualized the landscape of driver mutations, providing oncogenic insights in BRAF- and NRAS-driven melanoma as well as those without known NRAS/BRAF mutations. The landscape also clarified a mutational basis for RB and p53 pathway deregulation in this malignancy. Finally, the spectrum of driver mutations provided unequivocal genomic evidence for a direct mutagenic role of UV light in melanoma pathogenesis.

C1orf61 acts as a tumor activator in human hepatocellular carcinoma and is associated with tumorigenesis and metastasis

The genomic amplification of chromosome 1q long arm, the chromosomal region containing C1orf61, is a common event in human cancers. However, the expression pattern of chromosome 1 open reading frame 61 (C1orf61) in hepatocellular carcinoma (HCC) and its effects on HCC progression remain unclear. We have previously reported that C1orf61 is highly up-regulated during human embryogenesis. In this study, we report that C1orf61 expression is associated with the progression of liver disease. We found that C1orf61 is up-regulated in hepatic cirrhosis tissues and is further up-regulated in primary HCC tumors. Moreover, hepatitis B virus (HBV)-positive patients exhibited significantly higher levels of C1orf61 expression than HBV-negative patients. The evaluation of highly malignant HCC cell lines revealed high protein expression levels of C1orf61. Furthermore, the C1orf61 protein was found to be predominantly distributed within the cytoplasm. The ectopic expression of C1orf61 in the nonmalignant L02 cell line promoted cellular proliferation and colony formation in vitro, as well as cell cycle progression via the regulation of the expression of specific cell cycle-related proteins. In addition, the overexpression of C1orf61 in L02 cells facilitated cellular invasion and metastasis. The down-regulation of epithelial markers (E-cadherin and occludin) and the up-regulation of mesenchymal markers (N-cadherin, vimentin, and snail) suggested that the overexpression of C1orf61 induced the epithelial-mesenchymal transition (EMT) that is linked to metastasis. Taken together, our findings demonstrate, for the first time, the roles of C1orf61 in HCC tumorigenesis and metastasis.

Pseudogenes: newly discovered players in human cancer

Because they are generally noncoding and thus considered nonfunctional and unimportant, pseudogenes have long been neglected. Recent advances have established that the DNA of a pseudogene, the RNA transcribed from a pseudogene, or the protein translated from a pseudogene can have multiple, diverse functions and that these functions can affect not only their parental genes but also unrelated genes. Therefore, pseudogenes have emerged as a previously unappreciated class of sophisticated modulators of gene expression, with a multifaceted involvement in the pathogenesis of human cancer.

Mutational signatures of de-differentiation in functional non-coding regions of melanoma genomes

Much emphasis has been placed on the identification, functional characterization, and therapeutic potential of somatic variants in tumor genomes. However, the majority of somatic variants lie outside coding regions and their role in cancer progression remains to be determined. In order to establish a system to test the functional importance of non-coding somatic variants in cancer, we created a low-passage cell culture of a metastatic melanoma tumor sample. As a foundation for interpreting functional assays, we performed whole-genome sequencing and analysis of this cell culture, the metastatic tumor from which it was derived, and the patient-matched normal genomes. When comparing somatic mutations identified in the cell culture and tissue genomes, we observe concordance at the majority of single nucleotide variants, whereas copy number changes are more variable. To understand the functional impact of non-coding somatic variation, we leveraged functional data generated by the ENCODE Project Consortium. We analyzed regulatory regions derived from multiple different cell types and found that melanocyte-specific regions are among the most depleted for somatic mutation accumulation. Significant depletion in other cell types suggests the metastatic melanoma cells de-differentiated to a more basal regulatory state. Experimental identification of genome-wide regulatory sites in two different melanoma samples supports this observation. Together, these results show that mutation accumulation in metastatic melanoma is nonrandom across the genome and that a de-differentiated regulatory architecture is common among different samples. Our findings enable identification of the underlying genetic components of melanoma and define the differences between a tissue-derived tumor sample and the cell culture created from it. Such information helps establish a broader mechanistic understanding of the linkage between non-coding genomic variations and the cellular evolution of cancer.

Here we investigate the relationship between somatic variants and non-coding regulatory regions. To do this, we develop a new algorithm for identifying single nucleotide somatic variants in whole-genome sequencing data and apply it to a metastatic melanoma sample and a cell culture derived from this sample. Our results show that the two genomes are similar at the level of single nucleotide changes and more variable at larger copy number changes. We further observe that patterns of somatic mutation accumulation in non-coding regulatory regions suggests that the metastatic melanoma cells de-differentiated into a more basal regulatory state. That is, by simply looking at mutation accumulation across cell-type-specific non-coding functional regions, one can clearly see patterns that are indicative of cell state de-differentiation. Results from genome-wide functional regulatory region experimental mapping support this observation.

Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives

Gene fusions are important genomic events in human cancer because their fusion gene products can drive the development of cancer and thus are potential prognostic tools or therapeutic targets in anti-cancer treatment. Major advancements have been made in computational approaches for fusion gene discovery over the past 3 years due to improvements and widespread applications of high-throughput next generation sequencing (NGS) technologies. To identify fusions from NGS data, existing methods typically leverage the strengths of both sequencing technologies and computational strategies. In this article, we review the NGS and computational features of existing methods for fusion gene detection and suggest directions for future development.

Transcriptome profiling of the cancer, adjacent non-tumor and distant normal tissues from a colorectal cancer patient by deep sequencing

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. A genome-wide screening of transcriptome dysregulation between cancer and normal tissue would provide insight into the molecular basis of CRC initiation and progression. Compared with microarray technology, which is commonly used to identify transcriptional changes, the recently developed RNA-seq technique has the ability to detect other abnormal regulations in the cancer transcriptome, such as alternative splicing, novel transcripts or gene fusion. In this study, we performed high-throughput transcriptome sequencing at ∼50× coverage on CRC, adjacent non-tumor and distant normal tissue. The results revealed cancer-specific, differentially expressed genes and differential alternative splicing, suggesting that the extracellular matrix and metabolic pathways are activated and the genes related to cell homeostasis are suppressed in CRC. In addition, one tumor-restricted gene fusion, PRTEN-NOTCH2, was also detected and experimentally confirmed. This study reveals some common features in tumor invasion and provides a comprehensive survey of the CRC transcriptome, which provides better insight into the complexity of regulatory changes during tumorigenesis.

Paired-end sequencing of Fosmid libraries by Illumina

Eliminating the bacterial cloning step has been a major factor in the vastly improved efficiency of massively parallel sequencing approaches. However, this also has made it a technical challenge to produce the modern equivalent of the Fosmid- or BAC-end sequences that were crucial for assembling and analyzing complex genomes during the Sanger-based sequencing era. To close this technology gap, we developed Fosill, a method for converting Fosmids to Illumina-compatible jumping libraries. We constructed Fosmid libraries in vectors with Illumina primer sequences and specific nicking sites flanking the cloning site. Our family of pFosill vectors allows multiplex Fosmid cloning of end-tagged genomic fragments without physical size selection and is compatible with standard and multiplex paired-end Illumina sequencing. To excise the bulk of each cloned insert, we introduced two nicks in the vector, translated them into the inserts, and cleaved them. Recircularization of the vector via coligation of insert termini followed by inverse PCR generates a jumping library for paired-end sequencing with 101-base reads. The yield of unique Fosmid-sized jumps is sufficiently high, and the background of short, incorrectly spaced and chimeric artifacts sufficiently low, to enable applications such as mapping of structural variation and scaffolding of de novo assemblies. We demonstrate the power of Fosill to map genome rearrangements in a cancer cell line and identified three fusion genes that were corroborated by RNA-seq data. Our Fosill-powered assembly of the mouse genome has an N50 scaffold length of 17.0 Mb, rivaling the connectivity (16.9 Mb) of the Sanger-sequencing based draft assembly.

The three M's: melanoma, microphthalmia-associated transcription factor and microRNA

Studies examining intratumor heterogeneity have indicated that several cancer types, including melanoma, can display phenotypic plasticity, corresponding to their capacity to undergo transient reversible cellular changes. Conceptual models constructed to explain the process of cancer propagation differ in their treatment of intratumor heterogeneity. Recent observations of reversible phenotypic heterogeneity in melanoma have led to the proposal of a novel 'phenotypic plasticity' model of cancer propagation. Microphthalmia-associated transcription factor (MITF), the melanocyte 'lineage-specific' transcription factor, has emerged as one of the central players in melanoma phenotypic plasticity. Here we discuss the conceptual models suggested to explain the relations between MITF and melanoma plasticity, in addition to the complex regulatory roles that MITF plays in melanocytes and melanoma development. Finally, we provide an in-depth literature survey of microRNAs (miRNAs) involved in MITF activity, melanoma propagation and metastasis, in addition to their potential use as agents of personalized therapy.

nFuse: discovery of complex genomic rearrangements in cancer using high-throughput sequencing

Complex genomic rearrangements (CGRs) are emerging as a new feature of cancer genomes. CGRs are characterized by multiple genomic breakpoints and thus have the potential to simultaneously affect multiple genes, fusing some genes and interrupting other genes. Analysis of high-throughput whole-genome shotgun sequencing (WGSS) is beginning to facilitate the discovery and characterization of CGRs, but further development of computational methods is required. We have developed an algorithmic method for identifying CGRs in WGSS data based on shortest alternating paths in breakpoint graphs. Aiming for a method with the highest possible sensitivity, we use breakpoint graphs built from all WGSS data, including sequences with ambiguous genomic origin. Since the majority of cell function is encoded by the transcriptome, we target our search to find CGRs that underlie fusion transcripts predicted from matched high-throughput cDNA sequencing (RNA-seq). We have applied our method, nFuse, to the discovery of CGRs in publicly available data from the well-studied breast cancer cell line HCC1954 and primary prostate tumor sample 963. We first establish the sensitivity and specificity of the nFuse breakpoint prediction and scoring method using breakpoints previously discovered in HCC1954. We then validate five out of six CGRs in HCC1954 and two out of two CGRs in 963. We show examples of gene fusions that would be difficult to discover using methods that do not account for the existence of CGRs, including one important event that was missed in a previous study of the HCC1954 genome. Finally, we illustrate how CGRs may be used to infer the gene expression history of a tumor.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

The availability of the human genome sequence has allowed identification of disease-causing mutations in many Mendelian disorders, and detection of significant associations of nucleotide polymorphisms to complex diseases and traits. Despite these progresses, finding the causative variations for most of the common diseases remains a complex task. Several studies have shown gene expression analyses provide a quite unbiased way to investigate complex traits and common disorders' pathogenesis. Therefore, whole-transcriptome analysis is increasingly acquiring a key role in the knowledge of mechanisms responsible for complex diseases. Hybridization- and tag-based technologies have elucidated the involvement of multiple genes and pathways in pathological conditions, providing insights into the expression of thousand of coding and noncoding RNAs, such as microRNAs. However, the introduction of Next-Generation Sequencing, particularly of RNA-Seq, has overcome some drawbacks of previously used technologies. Identifying, in a single experiment, potentially novel genes/exons and splice isoforms, RNA editing, fusion transcripts and allele-specific expression are some of its advantages. RNA-Seq has been fruitfully applied to study cancer and host-pathogens interactions, and it is taking first steps for studying neurodegenerative diseases (ND) as well as neuropsychiatric diseases. In addition, it is emerging as a very powerful tool to study quantitative trait loci associated with gene expression in complex diseases. This paper provides an overview on gene expression profiling of complex diseases, with emphasis on RNA-Seq, its advantages over conventional technologies for studying cancer and ND, and for linking nucleotide variations to gene expression changes, also discussing its limitations.

Melanoma molecular classes and prognosis in the postgenomic era

Gene expression profiling is a powerful method to classify human tumours on the basis of biological aggressiveness, response to therapy, and outcome for the patient, but its application in melanoma lags behind that of other cancers. From more than 100 articles available on the topic, we selected 14 focusing on patients' outcome. We review and briefly discuss salient findings, and list ten reasons why melanoma molecular classes are not yet used in clinical diagnosis and prognosis. The available evidence suggests that we are on the verge of creating a framework for the use of melanoma molecular classes in prognosis, but so far there is little consensus to put together informative diagnostic and prognostic gene sets.

Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model

MOTIVATION

Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model.

RESULTS

We report the fusion genes discovered by the proposed framework on experimentally validated biological samples of chronic myelogenous leukemia (CML) and on public NCBI datasets, for which Bellerophontes is able to detect the exact junction sequence. With respect to state-of-art approaches, Bellerophontes detects the same experimentally validated fusions, however, it is more selective on the total number of detected fusions and provides a more accurate set of spanning reads supporting the junctions. We finally report the fusions involving non-annotated transcripts found in CML samples.

AVAILABILITY AND IMPLEMENTATION

Bellerophontes JAVA/Perl/Bash software implementation is free and available at http://eda.polito.it/bellerophontes/.

Conserved expression signatures between medaka and human pigment cell tumors

Aberrations in gene expression are a hallmark of cancer cells. Differential tumor-specific transcript levels of single genes or whole sets of genes may be critical for the neoplastic phenotype and important for therapeutic considerations or useful as biomarkers. As an approach to filter out such relevant expression differences from the plethora of changes noted in global expression profiling studies, we searched for changes of gene expression levels that are conserved. Transcriptomes from massive parallel sequencing of different types of melanoma from medaka were generated and compared to microarray datasets from zebrafish and human melanoma. This revealed molecular conservation at various levels between fish models and human tumors providing a useful strategy for identifying expression signatures strongly associated with disease phenotypes and uncovering new melanoma molecules.

De novo assembly and characterization of bark transcriptome using Illumina sequencing and development of EST-SSR markers in rubber tree (Hevea brasiliensis Muell. Arg.)

Background

In rubber tree, bark is one of important agricultural and biological organs. However, the molecular mechanism involved in the bark formation and development in rubber tree remains largely unknown, which is at least partially due to lack of bark transcriptomic and genomic information. Therefore, it is necessary to carried out high-throughput transcriptome sequencing of rubber tree bark to generate enormous transcript sequences for the functional characterization and molecular marker development.

Results

In this study, more than 30 million sequencing reads were generated using Illumina paired-end sequencing technology. In total, 22,756 unigenes with an average length of 485 bp were obtained with de novo assembly. The similarity search indicated that 16,520 and 12,558 unigenes showed significant similarities to known proteins from NCBI non-redundant and Swissprot protein databases, respectively. Among these annotated unigenes, 6,867 and 5,559 unigenes were separately assigned to Gene Ontology (GO) and Clusters of Orthologous Group (COG). When 22,756 unigenes searched against the Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) database, 12,097 unigenes were assigned to 5 main categories including 123 KEGG pathways. Among the main KEGG categories, metabolism was the biggest category (9,043, 74.75%), suggesting the active metabolic processes in rubber tree bark. In addition, a total of 39,257 EST-SSRs were identified from 22,756 unigenes, and the characterizations of EST-SSRs were further analyzed in rubber tree. 110 potential marker sites were randomly selected to validate the assembly quality and develop EST-SSR markers. Among 13 Hevea germplasms, PCR success rate and polymorphism rate of 110 markers were separately 96.36% and 55.45% in this study.

Conclusion

By assembling and analyzing de novo transcriptome sequencing data, we reported the comprehensive functional characterization of rubber tree bark. This research generated a substantial fraction of rubber tree transcriptome sequences, which were very useful resources for gene annotation and discovery, molecular markers development, genome assembly and annotation, and microarrays development in rubber tree. The EST-SSR markers identified and developed in this study will facilitate marker-assisted selection breeding in rubber tree. Moreover, this study also supported that transcriptome analysis based on Illumina paired-end sequencing is a powerful tool for transcriptome characterization and molecular marker development in non-model species, especially those with large and complex genomes.

BRAFV600E remodels the melanocyte transcriptome and induces BANCR to regulate melanoma cell migration

Aberrations of protein-coding genes are a focus of cancer genomics; however, the impact of oncogenes on expression of the ∼50% of transcripts without protein-coding potential, including long noncoding RNAs (lncRNAs), has been largely uncharacterized. Activating mutations in the BRAF oncogene are present in >70% of melanomas, 90% of which produce active mutant BRAF^V600E protein. To define the impacts of oncogenic BRAF on the melanocyte transcriptome, massively parallel cDNA sequencing (RNA-seq) was performed on genetically matched normal human melanocytes with and without BRAF^V600E expression. To enhance potential disease relevance by verifying expression of altered genes in BRAF-driven cancer tissue, parallel RNA-seq was also undertaken of two BRAF^V600E-mutant human melanomas. BRAF^V600E regulated expression of 1027 protein-coding transcripts and 39 annotated lncRNAs, as well as 70 unannotated, potentially novel, intergenic transcripts. These transcripts display both tissue-specific and multi-tissue expression profiles and harbor distinctive regulatory chromatin marks and transcription factor binding sites indicative of active transcription. Coding potential analysis of the 70 unannotated transcripts suggested that most may represent newly identified lncRNAs. BRAF-regulated lncRNA 1 (BANCR) was identified as a recurrently overexpressed, previously unannotated 693-bp transcript on chromosome 9 with a potential functional role in melanoma cell migration. BANCR knockdown reduced melanoma cell migration, and this could be rescued by the chemokine CXCL11. Combining RNA-seq of oncogene-expressing normal cells with RNA-seq of their corresponding human cancers may represent a useful approach to discover new oncogene-regulated RNA transcripts of potential clinical relevance in cancer.

Melanoma genome sequencing reveals frequent PREX2 mutations

Melanoma is notable for its metastatic propensity, lethality in the advanced setting and association with ultraviolet exposure early in life. To obtain a comprehensive genomic view of melanoma in humans, we sequenced the genomes of 25 metastatic melanomas and matched germline DNA. A wide range of point mutation rates was observed: lowest in melanomas whose primaries arose on non-ultraviolet-exposed hairless skin of the extremities (3 and 14 per megabase (Mb) of genome), intermediate in those originating from hair-bearing skin of the trunk (5-55 per Mb), and highest in a patient with a documented history of chronic sun exposure (111 per Mb). Analysis of whole-genome sequence data identified PREX2 (phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2)--a PTEN-interacting protein and negative regulator of PTEN in breast cancer--as a significantly mutated gene with a mutation frequency of approximately 14% in an independent extension cohort of 107 human melanomas. PREX2 mutations are biologically relevant, as ectopic expression of mutant PREX2 accelerated tumour formation of immortalized human melanocytes in vivo. Thus, whole-genome sequencing of human melanoma tumours revealed genomic evidence of ultraviolet pathogenesis and discovered a new recurrently mutated gene in melanoma.

BreakFusion: targeted assembly-based identification of gene fusions in whole transcriptome paired-end sequencing data

Despite recent progress, computational tools that identify gene fusions from next-generation whole transcriptome sequencing data are often limited in accuracy and scalability. Here, we present a software package, BreakFusion that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity and computational efficiency.

AVAILABILITY

http://bioinformatics.mdanderson.org/main/BreakFusion

NGS catalog: A database of next generation sequencing studies in humans

Next generation sequencing (NGS) technologies have been rapidly applied in biomedical and biological research since its advent only a few years ago, and they are expected to advance at an unprecedented pace in the following years. To provide the research community with a comprehensive NGS resource, we have developed the database Next Generation Sequencing Catalog (NGS Catalog, http://bioinfo.mc.vanderbilt.edu/NGS/index.html), a continually updated database that collects, curates and manages available human NGS data obtained from published literature. NGS Catalog deposits publication information of NGS studies and their mutation characteristics (SNVs, small insertions/deletions, copy number variations, and structural variants), as well as mutated genes and gene fusions detected by NGS. Other functions include user data upload, NGS general analysis pipelines, and NGS software. NGS Catalog is particularly useful for investigators who are new to NGS but would like to take advantage of these powerful technologies for their own research. Finally, based on the data deposited in NGS Catalog, we summarized features and findings from whole exome sequencing, whole genome sequencing, and transcriptome sequencing studies for human diseases or traits.

High throughput investigative Dermatology in 2012 and beyond: A new era beckons

High throughput molecular biology began around the mid-1990s with the introduction of microarrays - a technology that enabled investigators to quantify the cellular expression levels of tens of thousands of mRNA transcripts simultaneously. To date, a large number of microarray experiments have been performed in the investigation of RNA expression signatures in normal and pathological tissues. This review focuses on a next generation tool in high throughput investigation: RNA sequencing or RNA-Seq, highlighting its advantages over traditional microarray investigation and discussing its utility in investigative dermatology. In contrast with the results obtained from microarray experiments, RNA-Seq generates mRNA abundance counts, can identify novel transcripts and splice variants, and provides sequence resolution at the level of single base-pairs. Implementing RNA-Seq in the investigation of skin disease will yield novel insights into the pathogenesis of disease, will facilitate the discovery of new diseases and new mechanisms of disease, and will allow researchers to probe genetic disease in high resolution and with unprecedented efficiency.

RNA-Seq mapping and detection of gene fusions with a suffix array algorithm

High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample.

Advances in sequencing technology are enabling detailed characterization of RNA transcripts from biological samples. The fundamental challenge of accurately mapping the reads on transcripts and gleaning biological meaning from the data remains. One class of transcripts, gene fusions, is particularly important in cancer. Some gene fusions are prominent markers in leukemia, prostate, and other cancers and putatively causative in certain tumor types. We present a set of new RNA-Seq analysis techniques to map reads, and count expression of genes, exons and splicing junctions, especially those that give evidence of gene fusions. These tools are available in a software package with a straightforward graphical user interface. Using this software, we called and validated several gene fusions in a breast cancer cell line. By testing the presence of these fusions in a larger population of tumor cell lines and clinical samples, we found that two of them were expressed recurrently.