The RNA-binding proteins hnRNP H and F regulate splicing of a MYC-dependent HRAS exon in prostate cancer cells.

The MYC proto-oncogene contributes to the pathogenesis of more than half of human cancers. Malignant transformation by MYC transcriptionally up-regulates the core pre-mRNA splicing machinery and causes misregulation of alternative splicing. However, our understanding of how splicing changes are directed by MYC is limited. We performed a signaling pathway-guided splicing analysis to identify MYC-dependent splicing events. These included an HRAS cassette exon repressed by MYC across multiple tumor types. To molecularly dissect the regulation of this HRAS exon, we used antisense oligonucleotide tiling to identify splicing enhancers and silencers in its flanking introns. RNA-binding motif prediction indicated multiple binding sites for hnRNP H and hnRNP F within these cis-regulatory elements. Using siRNA knockdown and cDNA expression, we found that both hnRNP H and F activate the HRAS cassette exon. Mutagenesis and targeted RNA immunoprecipitation implicate two downstream G-rich elements in this splicing activation. Analyses of ENCODE RNA-seq datasets confirmed hnRNP H regulation of HRAS splicing. Analyses of RNA-seq datasets across multiple cancers showed a negative correlation of HNRNPH gene expression with MYC hallmark enrichment, consistent with the effect of hnRNP H on HRAS splicing. Interestingly, HNRNPF expression showed a positive correlation with MYC hallmarks and thus was not consistent with the observed effects of hnRNP F. Loss of hnRNP H/F altered cell cycle progression and induced apoptosis in the PC3 prostate cancer cell line. Collectively, our results reveal mechanisms for MYC-dependent regulation of splicing and point to possible therapeutic targets in prostate cancers.

Regional Variation of Splicing QTLs in Human Brain.

A major question in human genetics is how sequence variants of broadly expressed genes produce tissue- and cell type-specific molecular phenotypes. Genetic variation of alternative splicing is a prevalent source of transcriptomic and proteomic diversity in human populations. We investigated splicing quantitative trait loci (sQTLs) in 1,209 samples from 13 human brain regions, using RNA sequencing (RNA-seq) and genotype data from the Genotype-Tissue Expression (GTEx) project. Hundreds of sQTLs were identified in each brain region. Some sQTLs were shared across brain regions, whereas others displayed regional specificity. These "regionally ubiquitous" and "regionally specific" sQTLs showed distinct positional distributions of single-nucleotide polymorphisms (SNPs) within and outside essential splice sites, respectively, suggesting their regulation by distinct molecular mechanisms. Integrating the binding motifs and expression patterns of RNA binding proteins with exon splicing profiles, we uncovered likely causal variants underlying brain region-specific sQTLs. Notably, SNP rs17651213 created a putative binding site for the splicing factor RBFOX2 and was associated with increased splicing of MAPT exon 3 in cerebellar tissues, where RBFOX2 was highly expressed. Overall, our study reveals a more comprehensive spectrum and regional variation of sQTLs in human brain and demonstrates that such regional variation can be used to fine map potential causal variants of sQTLs and their associated neurological diseases.

Technology dictates algorithms: recent developments in read alignment.

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.

Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Benchmarking of computational error-correction methods for next-generation sequencing data.

BACKGROUND: Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. RESULTS: In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods. CONCLUSIONS: In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.

Transcriptome analysis in whole blood reveals increased microbial diversity in schizophrenia.

The role of the human microbiome in health and disease is increasingly appreciated. We studied the composition of microbial communities present in blood across 192 individuals, including healthy controls and patients with three disorders affecting the brain: schizophrenia, amyotrophic lateral sclerosis, and bipolar disorder. By using high-quality unmapped RNA sequencing reads as candidate microbial reads, we performed profiling of microbial transcripts detected in whole blood. We were able to detect a wide range of bacterial and archaeal phyla in blood. Interestingly, we observed an increased microbial diversity in schizophrenia patients compared to the three other groups. We replicated this finding in an independent schizophrenia case-control cohort. This increased diversity is inversely correlated with estimated cell abundance of a subpopulation of CD8+ memory T cells in healthy controls, supporting a link between microbial products found in blood, immunity and schizophrenia.

ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues.

High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki .