Incomplete annotation has a disproportionate impact on our understanding of Mendelian and complex neurogenetic disorders

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.

End-to-end RT-PCR of long RNA and highly structured RNA

RNA molecules play important roles in numerous normal cellular processes and disease states, from protein coding to gene regulation. RT-PCR, applying the power of polymerase chain reaction (PCR) to RNA by coupling reverse transcription with PCR, is one of the most important techniques to characterize RNA transcripts and monitor gene expression. The ability to analyze full-length RNA transcripts and detect their expression is critical to decipher their biological functions. However, due to the low processivity of retroviral reverse transcriptases (RTs), we can only monitor a small fraction of long RNA transcripts, especially those containing stable secondary and tertiary structures. The full-length sequences can only be deduced by computational analysis, which is often misleading. Group II intron-encoded RTs are a new type of RT enzymes. They have evolved specialized structural elements that unwind template structures and maintain close contact with the RNA template. Therefore, group II intron-encoded RTs are more processive than the retroviral RTs. The discovery, optimization and deployment of processive group II intron RTs provide us the opportunity to analyze RNA transcripts with single molecule resolution. MarathonRT, the most processive group II intron RT, has been extensively optimized for processive reverse transcription. In this chapter, we use MarathonRT to deliver a general protocol for long amplicon generation by RT-PCR, and also provide guidance for troubleshooting and further optimization.

Prioritizing genes associated with brain disorders by leveraging enhancer-promoter interactions in diverse neural cells and tissues

BACKGROUND

Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them.

METHODS

By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues.

RESULTS

We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied.

CONCLUSIONS

Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets.

CeGAL: Redefining a Widespread Fungal-Specific Transcription Factor Family Using an In Silico Error-Tracking Approach

In fungi, the most abundant transcription factor (TF) class contains a fungal-specific ‘GAL4-like’ Zn2C6 DNA binding domain (DBD), while the second class contains another fungal-specific domain, known as ‘fungal_trans’ or middle homology domain (MHD), whose function remains largely uncharacterized. Remarkably, almost a third of MHD-containing TFs in public sequence databases apparently lack DNA binding activity, since they are not predicted to contain a DBD. Here, we reassess the domain organization of these ‘MHD-only’ proteins using an in silico error-tracking approach. In a large-scale analysis of ~17,000 MHD-only TF sequences present in all fungal phyla except Microsporidia and Cryptomycota, we show that the vast majority (>90%) result from genome annotation errors and we are able to predict a new DBD sequence for 14,261 of them. Most of these sequences correspond to a Zn2C6 domain (82%), with a small proportion of C2H2 domains (4%) found only in Dikarya. Our results contradict previous findings that the MHD-only TF are widespread in fungi. In contrast, we show that they are exceptional cases, and that the fungal-specific Zn2C6–MHD domain pair represents the canonical domain signature defining the most predominant fungal TF family. We call this family CeGAL, after the highly characterized members: Cep3, whose 3D structure is determined, and GAL4, a eukaryotic TF archetype. We believe that this will not only improve the annotation and classification of the Zn2C6 TF but will also provide critical guidance for future fungal gene regulatory network analyses.

Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

Genomic regulatory elements active in the developing human brain are notably enriched in genetic risk for neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and bipolar disorder. However, prioritizing the specific risk genes and candidate molecular mechanisms underlying these genetic enrichments has been hindered by the lack of a single unified large-scale gene regulatory atlas of human brain development. Here, we uniformly process and systematically characterize gene, isoform, and splicing quantitative trait loci (xQTLs) in 672 fetal brain samples from unique subjects across multiple ancestral populations. We identify 15,752 genes harboring a significant xQTL and map 3,739 eQTLs to a specific cellular context. We observe a striking drop in gene expression and splicing heritability as the human brain develops. Isoform-level regulation, particularly in the second trimester, mediates the greatest proportion of heritability across multiple psychiatric GWAS, compared with eQTLs. Via colocalization and TWAS, we prioritize biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, nearly two-fold that observed in the adult brain. Finally, we build a comprehensive set of developmentally regulated gene and isoform co-expression networks capturing unique genetic enrichments across disorders. Together, this work provides a comprehensive view of genetic regulation across human brain development as well as the stage-and cell type-informed mechanistic underpinnings of neuropsychiatric disorders.

Revised eutherian gene collections

Objectives

The most recent research projects in scientific field of eutherian comparative genomics included intentions to sequence every extant eutherian species genome in foreseeable future, so that future revisions and updates of eutherian gene data sets were expected.

Data description

Using 35 public eutherian reference genomic sequence assemblies and free available software, the eutherian comparative genomic analysis protocol RRID:SCR_014401 was published as guidance against potential genomic sequence errors. The protocol curated 14 eutherian third-party data gene data sets, including, in aggregate, 2615 complete coding sequences that were deposited in European Nucleotide Archive. The published eutherian gene collections were used in revisions and updates of eutherian gene data set classifications and nomenclatures that included gene annotations, phylogenetic analyses and protein molecular evolution analyses.

ReadZS detects cell type-specific and developmentally regulated RNA processing programs in single-cell RNA-seq

RNA processing, including splicing and alternative polyadenylation, is crucial to gene function and regulation, but methods to detect RNA processing from single-cell RNA sequencing data are limited by reliance on pre-existing annotations, peak calling heuristics, and collapsing measurements by cell type. We introduce ReadZS, an annotation-free statistical approach to identify regulated RNA processing in single cells. ReadZS discovers cell type-specific RNA processing in human lung and conserved, developmentally regulated RNA processing in mammalian spermatogenesis-including global 3' UTR shortening in human spermatogenesis. ReadZS also discovers global 3' UTR lengthening in Arabidopsis development, highlighting the usefulness of this method in under-annotated transcriptomes.

Prediction of transcript isoforms in 19 chicken tissues by Oxford Nanopore long-read sequencing

To identify and annotate transcript isoforms in the chicken genome, we generated Nanopore long-read sequencing data from 68 samples that encompassed 19 diverse tissues collected from experimental adult male and female White Leghorn chickens. More than 23.8 million reads with mean read length of 790 bases and average quality of 18.2 were generated. The annotation and subsequent filtering resulted in the identification of 55,382 transcripts at 40,547 loci with mean length of 1,700 bases. We predicted 30,967 coding transcripts at 19,461 loci, and 16,495 lncRNA transcripts at 15,512 loci. Compared to existing reference annotations, we found ∼52% of annotated transcripts could be partially or fully matched while ∼47% were novel. Seventy percent of novel transcripts were potentially transcribed from lncRNA loci. Based on our annotation, we quantified transcript expression across tissues and found two brain tissues (i.e., cerebellum and cortex) expressed the highest number of transcripts and loci. Furthermore, ∼22% of the transcripts displayed tissue specificity with the reproductive tissues (i.e., testis and ovary) exhibiting the most tissue-specific transcripts. Despite our wide sampling, ∼20% of Ensembl reference loci were not detected. This suggests that deeper sequencing and additional samples that include different breeds, cell types, developmental stages, and physiological conditions, are needed to fully annotate the chicken genome. The application of Nanopore sequencing in this study demonstrates the usefulness of long-read data in discovering additional novel loci (e.g., lncRNA loci) and resolving complex transcripts (e.g., the longest transcript for the TTN locus).

Using genome-wide expression compendia to study microorganisms

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Simple Summary

The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components.

Abstract

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

FishExp: A comprehensive database and analysis platform for gene expression and alternative splicing of fish species

The publicly archived RNA-seq data has grown exponentially, while its valuable information has not yet been fully discovered and utilized, such as alternative splicing and its integration with gene expression. This is especially true for fish species which play important roles in ecology, research and the food industry. Furthermore, there is a lack of online platform to analyze users’ new data individually and jointly with existing data for the comprehensive analysis of alternative splicing and gene expression. Here, we present FishExp, a web-based data platform covering gene expression and alternative splicing in 26,081 RNA-seq experiments from 44 fishes. It allows users to query the data in a variety of ways, including gene identifier/symbol, functional term, and BLAST alignment. Moreover, users can customize experiments and tools to perform differential/specific expression and alternative splicing analysis, co-expression and cross-species analysis. In addition, functional enrichment is provided to confer biological significance. Notably, users are allowed to submit their own data and perform various analyses using the new data alone or alongside existing data in FishExp. Results of retrieval and analysis can be visualized on the gene-, transcript- and splicing event-level webpage in a highly interactive and intuitive manner. All data in FishExp can be downloaded for more in-depth analysis. The manually curated sample information, uniform data processing and various tools make it efficient for users to gain new insights from these large data sets, facilitating scientific hypothesis generation. FishExp is freely accessible at https://bioinfo.njau.edu.cn/fishExp.

An unexplored angle: T cell antigen discoveries reveal a marginal contribution of proteasome splicing to the immunogenic MHC class I antigen pool

In the current era of T cell–based immunotherapies, it is crucial to understand which types of MHC-presented T cell antigens are produced by tumor cells. In addition to linear peptide antigens, chimeric peptides are generated through proteasome-catalyzed peptide splicing (PCPS). Whether such spliced peptides are abundantly presented by MHC is highly disputed because of disagreement in computational analyses of mass spectrometry data of MHC-eluted peptides. Moreover, such mass spectrometric analyses cannot elucidate how much spliced peptides contribute to the pool of immunogenic antigens. In this Perspective, we explain the significance of knowing the contribution of spliced peptides for accurate analyses of peptidomes on one hand, and to serve as a potential source of targetable tumor antigens on the other hand. Toward a strategy for mass spectrometry independent estimation of the contribution of PCPS to the immunopeptidome, we first reviewed methodologies to identify MHC-presented spliced peptide antigens expressed by tumors. Data from these identifications allowed us to compile three independent datasets containing 103, 74, and 83 confirmed T cell antigens from cancer patients. Only 3.9%, 1.4%, and between 0% and 7.2% of these truly immunogenic antigens are produced by PCPS, therefore providing a marginal contribution to the pool of immunogenic tumor antigens. We conclude that spliced peptides will not serve as a comprehensive source to expand the number of targetable antigens for immunotherapies.

Dynamic and widespread control of poly(A) tail length during macrophage activation

The poly(A) tail enhances translation and transcript stability, and tail length is under dynamic control during cell state transitions. Tail regulation plays essential roles in translational timing and fertilization in early development, but poly(A) tail dynamics have not been fully explored in post-embryonic systems. Here, we examined the landscape and impact of tail length control during macrophage activation. Upon activation, more than 1500 mRNAs, including proinflammatory genes, underwent distinctive changes in tail lengths. Increases in tail length correlated with mRNA levels regardless of transcriptional activity, and many mRNAs that underwent tail extension encode proteins necessary for immune function and post-transcriptional regulation. Strikingly, we found that ZFP36, whose protein product destabilizes target transcripts, undergoes tail extension. Our analyses indicate that many mRNAs undergoing tail lengthening are, in turn, degraded by elevated levels of ZFP36, constituting a post-transcriptional feedback loop that ensures transient regulation of transcripts integral to macrophage activation. Taken together, this study establishes the complexity, relevance, and widespread nature of poly(A) tail dynamics, and the resulting post-transcriptional regulation during macrophage activation.

Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes

There is growing evidence for the importance of 3' untranslated region (3'UTR) dependent regulatory processes. However, our current human 3'UTR catalogue is incomplete. Here, we develop a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3'UTRs. We identify unannotated 3'UTRs associated with 1,563 genes across 39 human tissues, with the greatest abundance found in the brain. These unannotated 3'UTRs are significantly enriched for RNA binding protein (RBP) motifs and exhibit high human lineage-specificity. We find that brain-specific unannotated 3'UTRs are enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes are involved in synaptic function. Our data is shared through an online resource F3UTER ( https://astx.shinyapps.io/F3UTER/ ). Overall, our data improves 3'UTR annotation and provides additional insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.

An alternative splicing hypothesis for neuropathology of schizophrenia: evidence from studies on historical candidate genes and multi-omics data

Alternative splicing of schizophrenia risk genes, such as DRD2, GRM3, and DISC1, has been extensively described. Nevertheless, the alternative splicing characteristics of the growing number of schizophrenia risk genes identified through genetic analyses remain relatively opaque. Recently, transcriptomic analyses in human brains based on short-read RNA-sequencing have discovered many "local splicing" events (e.g., exon skipping junctions) associated with genetic risk of schizophrenia, and further molecular characterizations have identified novel spliced isoforms, such as AS3MT^d2d3 and ZNF804A^E3E4. In addition, long-read sequencing analyses of schizophrenia risk genes (e.g., CACNA1C and NRXN1) have revealed multiple previously unannotated brain-abundant isoforms with therapeutic potentials, and functional analyses of KCNH2-3.1 and Ube3a1 have provided examples for investigating such spliced isoforms in vitro and in vivo. These findings suggest that alternative splicing may be an essential molecular mechanism underlying genetic risk of schizophrenia, however, the incomplete annotations of human brain transcriptomes might have limited our understanding of schizophrenia pathogenesis, and further efforts to elucidate these transcriptional characteristics are urgently needed to gain insights into the illness-correlated brain physiology and pathology as well as to translate genetic discoveries into novel therapeutic targets.

recount3: summaries and queries for large-scale RNA-seq expression and splicing

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio .

Spliceator: multi-species splice site prediction using convolutional neural networks

Background

Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking.

Results

We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms.

Conclusions

Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04471-3.

Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing

Alternative splicing is a post-transcriptional regulatory mechanism producing distinct mRNA molecules from a single pre-mRNA with a prominent role in the development and function of the central nervous system. We used long-read isoform sequencing to generate full-length transcript sequences in the human and mouse cortex. We identify novel transcripts not present in existing genome annotations, including transcripts mapping to putative novel (unannotated) genes and fusion transcripts incorporating exons from multiple genes. Global patterns of transcript diversity are similar between human and mouse cortex, although certain genes are characterized by striking differences between species. We also identify developmental changes in alternative splicing, with differential transcript usage between human fetal and adult cortex. Our data confirm the importance of alternative splicing in the cortex, dramatically increasing transcriptional diversity and representing an important mechanism underpinning gene regulation in the brain. We provide transcript-level data for human and mouse cortex as a resource to the scientific community.

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There is widespread transcript diversity in the cortex and many novel transcripts

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Some genes display big differences in isoform number between human and mouse cortex

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There is evidence of differential transcript usage between human fetal and adult cortex

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There are many novel isoforms of genes associated with human brain disease

SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses

BACKGROUND

RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step-such as alignment of reads to a reference genome-of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses.

RESULTS

In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided ( http://research.libd.org/SPEAQeasy/ ).

CONCLUSIONS

SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.

Human-lineage-specific genomic elements are associated with neurodegenerative disease and APOE transcript usage

Knowledge of genomic features specific to the human lineage may provide insights into brain-related diseases. We leverage high-depth whole genome sequencing data to generate a combined annotation identifying regions simultaneously depleted for genetic variation (constrained regions) and poorly conserved across primates. We propose that these constrained, non-conserved regions (CNCRs) have been subject to human-specific purifying selection and are enriched for brain-specific elements. We find that CNCRs are depleted from protein-coding genes but enriched within lncRNAs. We demonstrate that per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs. We find that genes implicated in neurological diseases have high CNCR density, including APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we show the intron-3-retaining transcript to be more abundant in Alzheimer's disease with more severe tau and amyloid pathological burden. Thus, we demonstrate potential association of human-lineage-specific sequences in brain development and neurological disease.

Global network analysis in Schizosaccharomyces pombe reveals three distinct consequences of the common 1-kb deletion causing juvenile CLN3 disease

Juvenile CLN3 disease is a recessively inherited paediatric neurodegenerative disorder, with most patients homozygous for a 1-kb intragenic deletion in CLN3. The btn1 gene is the Schizosaccharomyces pombe orthologue of CLN3. Here, we have extended the use of synthetic genetic array (SGA) analyses to delineate functional signatures for two different disease-causing mutations in addition to complete deletion of btn1. We show that genetic-interaction signatures can differ for mutations in the same gene, which helps to dissect their distinct functional effects. The mutation equivalent to the minor transcript arising from the 1-kb deletion (btn1^102–208del) shows a distinct interaction pattern. Taken together, our results imply that the minor 1-kb deletion transcript has three consequences for CLN3: to both lose and retain some inherent functions and to acquire abnormal characteristics. This has particular implications for the therapeutic development of juvenile CLN3 disease. In addition, this proof of concept could be applied to conserved genes for other mendelian disorders or any gene of interest, aiding in the dissection of their functional domains, unpacking the global consequences of disease pathogenesis, and clarifying genotype–phenotype correlations. In doing so, this detail will enhance the goals of personalised medicine to improve treatment outcomes and reduce adverse events.

Megadepth: efficient coverage quantification for BigWigs and BAMs

MOTIVATION

A common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types.

RESULTS

Megadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19 000 GTExV8 BigWig files in approximately 1 h using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular classification of zebrafish retinal ganglion cells links genes to cell types to behavior

Retinal ganglion cells (RGCs) form an array of feature detectors, which convey visual information to central brain regions. Characterizing RGC diversity is required to understand the logic of the underlying functional segregation. Using single-cell transcriptomics, we systematically classified RGCs in adult and larval zebrafish, thereby identifying marker genes for >30 mature types and several developmental intermediates. We used this dataset to engineer transgenic driver lines, enabling specific experimental access to a subset of RGC types. Expression of one or few transcription factors often predicts dendrite morphologies and axonal projections to specific tectal layers and extratectal targets. In vivo calcium imaging revealed that molecularly defined RGCs exhibit specific functional tuning. Finally, chemogenetic ablation of eomesa+ RGCs, which comprise melanopsin-expressing types with projections to a small subset of central targets, selectively impaired phototaxis. Together, our study establishes a framework for systematically studying the functional architecture of the visual system.