RNASEQR--a streamlined and accurate RNA-seq sequence analysis program

GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis

Big genomic data and artificial intelligence (AI) are ushering in an era of precision medicine, providing opportunities to study previously under-represented subtypes and rare diseases rather than categorize them as variances. However, clinical researchers face challenges in accessing such novel technologies as well as reliable methods to study small datasets or subcohorts with unique phenotypes. To address this need, we developed an integrative approach, GAiN, to capture patterns of gene expression from small datasets on the basis of an ensemble of generative adversarial networks (GANs) while leveraging big population data. Where conventional biostatistical methods fail, GAiN reliably discovers differentially expressed genes (DEGs) and enriched pathways between two cohorts with limited numbers of samples (n = 10) when benchmarked against a gold standard. GAiN is freely available at GitHub. Thus, GAiN may serve as a crucial tool for gene expression analysis in scenarios with limited samples, as in the context of rare diseases, under-represented populations, or limited investigator resources.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research

The comprehensive and integrative analysis of RNA-seq data, in different molecular layers from diverse samples, holds promise to address the full-scale complexity of biological systems. Recent advances in gene set variant analysis (GSVA) are providing exciting opportunities for revealing the specific biological processes of cancer samples. However, it is still urgently needed to develop a tool, which combines GSVA and different molecular characteristic analysis, as well as prognostic characteristics of cancer patients to reveal the biological processes of disease comprehensively. Here, we develop ARMT, an automatic tool for RNA-Seq data analysis. ARMT is an efficient and integrative tool with user-friendly interface to analyze related molecular characters of single gene and gene set comprehensively based on transcriptome and genomic data, which builds the bridge for deeper information between genes and pathways, to further accelerate scientific findings. ARMT can be installed easily from https://github.com/Dulab2020/ARMT.

Thirty years of neuroendocrinology: Technological advances pave the way for molecular discovery

Since the 1950s, the systems level interactions between the hypothalamus, pituitary and end organs such as the adrenal, thyroid and gonads have been well known; however, it is only over the last three decades that advances in molecular biology and information technology have provided a tremendous expansion of knowledge at the molecular level. Neuroendocrinology has benefitted from developments in molecular genetics, epigenetics and epigenomics, and most recently optogenetics and pharmacogenetics. This has enabled a new understanding of gene regulation, transcription, translation and post-translational regulation, which should help direct the development of drugs to treat neuroendocrine-related diseases.

Olfactory Receptors as Biomarkers in Human Breast Carcinoma Tissues

Olfactory receptors (ORs) are known to be expressed in a variety of human tissues and act on different physiological processes, such as cell migration, proliferation, or secretion and have been found to function as biomarkers for carcinoma tissues of prostate, lung, and small intestine. In this study, we analyzed the OR expression profiles of several different carcinoma tissues, with a focus on breast cancer. The expression of OR2B6 was detectable in breast carcinoma tissues; here, transcripts of OR2B6 were detected in 73% of all breast carcinoma cell lines and in over 80% of all of the breast carcinoma tissues analyzed. Interestingly, there was no expression of OR2B6 observed in healthy tissues. Immunohistochemical staining of OR2B6 in breast carcinoma tissues revealed a distinct staining pattern of carcinoma cells. Furthermore, we detected a fusion transcript containing part of the coding exon of OR2B6 as a part of a splice variant of the histone HIST1H2BO transcript. In addition, in cancer tissues and cell lines derived from lung, pancreas, and brain, OR expression patterns were compared to that of corresponding healthy tissues. The number of ORs detected in lung carcinoma tissues was significantly reduced in comparison to the surrounding healthy tissues. In pancreatic carcinoma tissues, OR4C6 was considerably more highly expressed in comparison to the respective healthy tissues. We detected OR2B6 as a potential biomarker for breast carcinoma tissues.

Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing

Low-input RNA-seq is powerful to represent the gene expression profiles with limited number of cells, especially when single-cell variations are not the aim. However, pre-amplification-based and molecule index-based library construction methods boost bias or require higher throughput. Here we demonstrate a simple, low-cost, low-bias and low-input RNA-seq with ion torrent semiconductor sequencing (LIEA RNA-seq). We also developed highly accurate and error-tolerant spliced mapping algorithm FANSe2splice to accurately map the single-ended reads to the reference genome with better experimental verifiability than the previous spliced mappers. Combining the experimental and computational advancements, our solution is comparable with the bulk mRNA-seq in quantification, reliably detects splice junctions and minimizes the bias with much less mappable reads.

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms

Background

Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared.

Results

Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours.

Conclusions

In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.

A comprehensive next generation sequencing-based virome assessment in brain tissue suggests no major virus - tumor association

Next generation sequencing (NGS) can globally interrogate the genetic composition of biological samples in an unbiased yet sensitive manner. The objective of this study was to utilize the capabilities of NGS to investigate the reported association between glioblastoma multiforme (GBM) and human cytomegalovirus (HCMV). A large-scale comprehensive virome assessment was performed on publicly available sequencing datasets from the Cancer Genome Atlas (TCGA), including RNA-seq datasets from primary GBM (n = 157), recurrent GBM (n = 13), low-grade gliomas (n = 514), recurrent low-grade gliomas (n = 17), and normal brain (n = 5), and whole genome sequencing (WGS) datasets from primary GBM (n = 51), recurrent GBM (n = 10), and normal matched blood samples (n = 20). In addition, RNA-seq datasets from MRI-guided biopsies (n = 92) and glioma stem-like cell cultures (n = 9) were analyzed. Sixty-four DNA-seq datasets from 11 meningiomas and their corresponding blood control samples were also analyzed. Finally, three primary GBM tissue samples were obtained, sequenced using RNA-seq, and analyzed. After in-depth analysis, the most robust virus findings were the detection of papillomavirus (HPV) and hepatitis B reads in the occasional LGG sample (4 samples and 1 sample, respectively). In addition, low numbers of virus reads were detected in several datasets but detailed investigation of these reads suggest that these findings likely represent artifacts or non-pathological infections. For example, all of the sporadic low level HCMV reads were found to map to the immediate early promoter intimating that they likely originated from laboratory expression vector contamination. Despite the detection of low numbers of Epstein-Barr virus reads in some samples, these likely originated from infiltrating B-cells. Finally, human herpesvirus 6 and 7 aligned viral reads were identified in all DNA-seq and a few RNA-seq datasets but detailed analysis demonstrated that these were likely derived from the homologous human telomeric-like repeats. Other low abundance viral reads were detected in some samples but for most viruses, the reads likely represent artifacts or incidental infections. This analysis argues against associations between most known viruses and GBM or mengiomas. Nevertheless, there may be a low percentage association between HPV and/or hepatitis B and LGGs.

Identification of allelic expression imbalance genes in human hepatocellular carcinoma through massively parallel DNA and RNA sequencing

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. The prognosis and treatment of this disease have changed little in recent decades because the mechanisms underlying most events of this disease remain obscure. Allelic variation of gene expression is associated with many important biological processes, which provide a new perspective to understand HCC pathogenesis at the molecular level. To identify allelic expression imbalance (AEI) genes in HCCs, we developed a computational method that considered accurate mapping and vigorous AEI detection using paired DNA-seq and RNA-seq data. We analyzed the DNA-seq and RNA-seq data derived from two HCC samples and two cell lines. By applying a strict criterion, a total of 203 tumor-specific AEI genes were identified with high confidence, and several genes have been reported to be associated with the migration or proliferation of cancer cells, such as the genes RELN and DHRS3. In addition, we also found some novel AEI genes in HCCs, such as HNRNPR and PTAFR. Our study provides new insight into AEI events that may contribute to understanding gene expression regulation, cell proliferation and migration, and tumorigenesis.

Selective capture of transcribed sequences in the functional gene analysis of microbial pathogens

Selective capture of transcribed sequences (SCOTS) is an effective method to identify bacterial genes differentially expressed during different biological processes, including pathogenic interactions with a host species. The method can be used to elucidate molecular mechanisms driving and maintaining such interactions. The method is a powerful genetic tool that overcomes limitations found in other methods, by working with small amounts of mRNA and allowing for the separation of bacterial cDNA from host cDNA. It has been increasingly used in the discovery of genes involved in the bacterium-host interaction. In this review, we briefly introduce the SCOTS method, outline the technical advances offered in the method, and focus on the method's applications in several microbial pathogens.

RNA-Seq identifies key reproductive gene expression alterations in response to cadmium exposure

Cadmium is a common toxicant that is detrimental to many tissues. Although a number of transcriptional signatures have been revealed in different tissues after cadmium treatment, the genes involved in the cadmium caused male reproductive toxicity, and the underlying molecular mechanism remains unclear. Here we observed that the mice treated with different amount of cadmium in their rodent chow for six months exhibited reduced serum testosterone. We then performed RNA-seq to comprehensively investigate the mice testicular transcriptome to further elucidate the mechanism. Our results showed that hundreds of genes expression altered significantly in response to cadmium treatment. In particular, we found several transcriptional signatures closely related to the biological processes of regulation of hormone, gamete generation, and sexual reproduction, respectively. The expression of several testosterone synthetic key enzyme genes, such as Star, Cyp11a1, and Cyp17a1, were inhibited by the cadmium exposure. For better understanding of the cadmium-mediated transcriptional regulatory mechanism of the genes, we computationally analyzed the transcription factors binding sites and the mircoRNAs targets of the differentially expressed genes. Our findings suggest that the reproductive toxicity by cadmium exposure is implicated in multiple layers of deregulation of several biological processes and transcriptional regulation in mice.

Methods to study splicing from high-throughput RNA sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data, which could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.

Reliable identification of genomic variants from RNA-seq data

Identifying genomic variation is a crucial step for unraveling the relationship between genotype and phenotype and can yield important insights into human diseases. Prevailing methods rely on cost-intensive whole-genome sequencing (WGS) or whole-exome sequencing (WES) approaches while the identification of genomic variants from often existing RNA sequencing (RNA-seq) data remains a challenge because of the intrinsic complexity in the transcriptome. Here, we present a highly accurate approach termed SNPiR to identify SNPs in RNA-seq data. We applied SNPiR to RNA-seq data of samples for which WGS and WES data are also available and achieved high specificity and sensitivity. Of the SNPs called from the RNA-seq data, >98% were also identified by WGS or WES. Over 70% of all expressed coding variants were identified from RNA-seq, and comparable numbers of exonic variants were identified in RNA-seq and WES. Despite our method's limitation in detecting variants in expressed regions only, our results demonstrate that SNPiR outperforms current state-of-the-art approaches for variant detection from RNA-seq data and offers a cost-effective and reliable alternative for SNP discovery.

Mining RNA-seq data for infections and contaminations

RNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression other than the host cell and are little equipped to address the problems arising from redundancies and gaps among sequenced microbe and virus genomes. We show that screening of sequencing reads for contaminations and infections can be performed easily using ContextMap, our recently developed mapping software. Based on mapping-derived statistics, mapping confidence, similarities and misidentifications (e.g. due to missing genome sequences) of species/strains can be assessed. Performance of our approach is evaluated on three real-life sequencing data sets and compared to state-of-the-art metagenomics tools. In particular, ContextMap vastly outperformed GASiC and GRAMMy in terms of runtime. In contrast to MEGAN4, it was capable of providing individual read mappings to species and resolving non-unique mappings, thus allowing the identification of misalignments caused by sequence similarities between genomes and missing genome sequences. Our study illustrates the importance and potentials of routinely mining RNA-seq experiments for infections or contaminations by microbes and viruses. By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.

DBATE: database of alternative transcripts expression

The use of high-throughput RNA sequencing technology (RNA-seq) allows whole transcriptome analysis, providing an unbiased and unabridged view of alternative transcript expression. Coupling splicing variant-specific expression with its functional inference is still an open and difficult issue for which we created the DataBase of Alternative Transcripts Expression (DBATE), a web-based repository storing expression values and functional annotation of alternative splicing variants. We processed 13 large RNA-seq panels from human healthy tissues and in disease conditions, reporting expression levels and functional annotations gathered and integrated from different sources for each splicing variant, using a variant-specific annotation transfer pipeline. The possibility to perform complex queries by cross-referencing different functional annotations permits the retrieval of desired subsets of splicing variant expression values that can be visualized in several ways, from simple to more informative. DBATE is intended as a novel tool to help appreciate how, and possibly why, the transcriptome expression is shaped.

Database URL:http://bioinformatica.uniroma2.it/DBATE/.

A comprehensive evaluation of alignment algorithms in the context of RNA-seq

Transcriptome sequencing (RNA-Seq) overcomes limitations of previously used RNA quantification methods and provides one experimental framework for both high-throughput characterization and quantification of transcripts at the nucleotide level. The first step and a major challenge in the analysis of such experiments is the mapping of sequencing reads to a transcriptomic origin including the identification of splicing events. In recent years, a large number of such mapping algorithms have been developed, all of which have in common that they require algorithms for aligning a vast number of reads to genomic or transcriptomic sequences. Although the FM-index based aligner Bowtie has become a de facto standard within mapping pipelines, a much larger number of possible alignment algorithms have been developed also including other variants of FM-index based aligners. Accordingly, developers and users of RNA-seq mapping pipelines have the choice among a large number of available alignment algorithms. To provide guidance in the choice of alignment algorithms for these purposes, we evaluated the performance of 14 widely used alignment programs from three different algorithmic classes: algorithms using either hashing of the reference transcriptome, hashing of reads, or a compressed FM-index representation of the genome. Here, special emphasis was placed on both precision and recall and the performance for different read lengths and numbers of mismatches and indels in a read. Our results clearly showed the significant reduction in memory footprint and runtime provided by FM-index based aligners at a precision and recall comparable to the best hash table based aligners. Furthermore, the recently developed Bowtie 2 alignment algorithm shows a remarkable tolerance to both sequencing errors and indels, thus, essentially making hash-based aligners obsolete.

OSA: a fast and accurate alignment tool for RNA-Seq

Accurately mapping RNA-Seq reads to the reference genome is a critical step for performing downstream analysis such as transcript assembly, isoform detection and quantification. Many tools have been developed; however, given the huge size of the next generation sequencing datasets and the complexity of the transcriptome, RNA-Seq read mapping remains a challenge with the ever-increasing amount of data. We develop Omicsoft sequence aligner (OSA), a fast and accurate alignment tool for RNA-Seq data. Benchmarked with existing methods, OSA improves mapping speed 4-10-fold with better sensitivity and less false positives.

AVAILABILITY

OSA can be downloaded from http://omicsoft.com/osa. It is free to academic users. OSA has been tested extensively on Linux, Mac OS X and Windows platforms.

A context-based approach to identify the most likely mapping for RNA-seq experiments

Background

Sequencing of mRNA (RNA-seq) by next generation sequencing technologies is widely used for analyzing the transcriptomic state of a cell. Here, one of the main challenges is the mapping of a sequenced read to its transcriptomic origin. As a simple alignment to the genome will fail to identify reads crossing splice junctions and a transcriptome alignment will miss novel splice sites, several approaches have been developed for this purpose. Most of these approaches have two drawbacks. First, each read is assigned to a location independent on whether the corresponding gene is expressed or not, i.e. information from other reads is not taken into account. Second, in case of multiple possible mappings, the mapping with the fewest mismatches is usually chosen which may lead to wrong assignments due to sequencing errors.

Results

To address these problems, we developed ContextMap which efficiently uses information on the context of a read, i.e. reads mapping to the same expressed region. The context information is used to resolve possible ambiguities and, thus, a much larger degree of ambiguities can be allowed in the initial stage in order to detect all possible candidate positions. Although ContextMap can be used as a stand-alone version using either a genome or transcriptome as input, the version presented in this article is focused on refining initial mappings provided by other mapping algorithms. Evaluation results on simulated sequencing reads showed that the application of ContextMap to either TopHat or MapSplice mappings improved the mapping accuracy of both initial mappings considerably.

Conclusions

In this article, we show that the context of reads mapping to nearby locations provides valuable information for identifying the best unique mapping for a read. Using our method, mappings provided by other state-of-the-art methods can be refined and alignment accuracy can be further improved.

Availability

http://www.bio.ifi.lmu.de/ContextMap.