Alejandra Cervera

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Erratum to: A survey of best practices for RNA-seq data analysis

Erratum During editing of the article by Conesa et al. [1], an error was introduced to some of the citations, such that incorrect references were provided for some articles the second time they were cited. The following sentences are affected: Algorithms that quantify expression from transcriptome mappings include RSEM (RNA-Seq by Expectation Maximization) [40], eXpress [41], Sailfish [35] and kallisto [42] among others. These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43]. The citation for Sailfish should be [34] (Patro et al., Nat Biotechnol. 2014;32:463–4) in both sentences. Additional factors that interfere with intra-sample comparisons include changes in transcript length across samples or conditions [50], positional biases in coverage along the transcript (which are accounted for in Cufflinks), average fragment size [43], and the GC contents of genes (corrected in the EDAseq package [21]). The citation for EDAseq should be [20] (Risso et al. BMC Bioinformatics. 2011;12:480) The NOISeq R package [20] contains a wide variety of diagnostic plots to identify sources of biases in RNA-seq data and to apply appropriate normalization procedures in each case. The citation for NOISeq should be [19] (Tarazona et al. Nucleic Acids Res. 2015;43:e140) These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].

Authorship attribution as a case of anomaly detection: A neural network model

Writings by the same author usually share specific traits, the so-called stylome, which is defined as an abstraction of the constraints and specific sequences of words and phrases used in the texts. Although identifying a stylome has been elusive, some advancements in this area have been made. Here, we present a system trained with texts from a given author that then unveiled some of its features and, in turn, detected texts not written by that author, or written within a different style. The system is based on time series processing capabilities of an unsupervised neural network model known as the self-organizing map. The core idea is that a system trained with texts by one author should detect an anomaly when presented with texts from other authors. We present results of authorship identification in several contexts including known benchmarks as well as some examples from literature, journalism, and popular science.

Integrative Analysis of Deep Sequencing Data Identifies Estrogen Receptor Early Response Genes and Links ATAD3B to Poor Survival in Breast Cancer

Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor (), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 early responsive genes and compared the biological functions of the genes having binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of via the MAPK pathway, instead of direct binding, may be responsible for early cell responses to activation. Our results also indicate that the responsive genes triggered by the genomic pathway are transcribed faster than those without binding sites. The survival analysis of the 777 responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.

SePIA: RNA and small RNA sequence processing, integration, and analysis

BackgroundLarge-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types.ResultsWe developed SePIA (Sequence Processing, Integration, and Analysis), a comprehensive small RNA and RNA workflow. It provides ready execution for over 20 commonly known RNA-seq tools on top of an established workflow engine and provides dynamic pipeline architecture to manage, individually analyze, and integrate both small RNA and RNA data. Implementation with Docker makes SePIA portable and easy to run. We demonstrate the workflow’s extensive utility with two case studies involving three breast cancer datasets. SePIA is straightforward to configure and organizes results into a perusable HTML report. Furthermore, the underlying pipeline engine supports computational resource management for optimal performance.ConclusionSePIA is an open-source workflow introducing standardized processing and analysis of RNA and small RNA data. SePIA’s modular design enables robust customization to a given experiment while maintaining overall workflow structure. It is available at http://anduril.org/sepia.

Modified penetrance of coding variants by cis-regulatory variation shapes human traits

Coding variants represent many of the strongest associations between genotype and phenotype, however they exhibit inter-individual differences in effect, known as variable penetrance. In this work, we study how cis-regulatory variation modifies the penetrance of coding variants in their target gene. Using functional genomic and genetic data from GTEx, we observed that in the general population, purifying selection has depleted haplotype combinations that lead to higher penetrance of pathogenic coding variants. Conversely, in cancer and autism patients, we observed an enrichment of haplotype combinations that lead to higher penetrance of pathogenic coding variants in disease implicated genes, which provides direct evidence that regulatory haplotype configuration of causal coding variants affects disease risk. Finally, we experimentally demonstrated that a regulatory variant can modify the penetrance of a coding variant by introducing a Mendelian SNP using CRISPR/Cas9 on distinct expression haplotypes and using the transcriptome as a phenotypic readout. Our results demonstrate that joint effects of regulatory and coding variants are an important part of the genetic architecture of human traits, and contribute to modified penetrance of disease-causing variants.

Alternative splicing discriminates molecular subtypes and has prognostic impact in diffuse large B-cell lymphoma

Effect of alternative splicing (AS) on diffuse large B-cell lymphoma (DLBCL) pathogenesis and survival has not been systematically addressed. Here, we compared differentially expressed genes and exons in association with survival after chemoimmunotherapy, and between germinal center B-cell like (GCB) and activated B-cell like (ABC) DLBCLs. Genome-wide exon array-based screen was performed from samples of 38 clinically high-risk patients who were treated in a Nordic phase II study with dose-dense chemoimmunotherapy and central nervous system prophylaxis. The exon expression profile separated the patients according to molecular subgroups and survival better than the gene expression profile. Pathway analyses revealed enrichment of AS genes in inflammation and adhesion-related processes, and in signal transduction, such as phosphatidylinositol signaling system and adenosine triphosphate binding cassette transporters. Altogether, 49% of AS-related exons were protein coding, and domain prediction showed 28% of such exons to include a functional domain, such as transmembrane helix domain or phosphorylation sites. Validation in an independent cohort of 92 DLBCL samples subjected to RNA-sequencing confirmed differential exon usage of selected genes and association of AS with molecular subtypes and survival. The results indicate that AS events are able to discriminate GCB and ABC DLBCLs and have prognostic impact in DLBCL.

MicroRNAs regulate key cell survival pathways and mediate chemosensitivity during progression of diffuse large B-cell lymphoma

Despite better therapeutic options and improved survival of diffuse large B-cell lymphoma (DLBCL), 30–40% of the patients experience relapse or have primary refractory disease with a dismal prognosis. To identify biological correlates for treatment resistance, we profiled microRNAs (miRNAs) of matched primary and relapsed DLBCL by next-generation sequencing. Altogether 492 miRNAs were expressed in the DLBCL samples. Thirteen miRNAs showed significant differential expression between primary and relapse specimen pairs. Integration of the differentially expressed miRNAs with matched mRNA expression profiles identified highly anti-correlated, putative targets, which were significantly enriched in cancer-associated pathways, including phosphatidylinositol (PI)), mitogen-activated protein kinase (MAPK), and B-cell receptor (BCR) signaling. Expression data suggested activation of these pathways during disease progression, and functional analyses validated that miR-370-3p, miR-381-3p, and miR-409-3p downregulate genes on the PI, MAPK, and BCR signaling pathways, and enhance chemosensitivity of DLBCL cells in vitro. High expression of selected target genes, that is, PIP5K1 and IMPA1, was found to be associated with poor survival in two independent cohorts of chemoimmunotherapy-treated patients (n = 92 and n = 233). Taken together, our results demonstrate that differentially expressed miRNAs contribute to disease progression by regulating key cell survival pathways and by mediating chemosensitivity, thus representing potential novel therapeutic targets.

Detection of different authorship of text sequences through self-organizing maps and mutual information function

Writers tend to express their ideas with different styles, defined with the so called firm or stylome, which is an abstraction of the general constraints and specific combinations of words within their language they decide to follow. Although capturing this style has proven to be very difficult, some advances have been achieved. Here, we present a novel system that is trained with texts from the same author, and is able to unveil some of its features, and to apply them to detect texts not written by the same author, or, at least, not written with the previously learned features. The system is an hybrid model based in self-organizing maps and in information-theoretic aspects. In the model, mutual information function of unknown texts are compared to the mutual information function of texts from a known author. If the distance between these two distributions exceeds a certain threshold, then the unknown text is from a different author, otherwise the authorship is the same. The decision threshold is obtained by the self-organizing map trained with the texts from the same author. We present results in authorship identification in several contexts including classic literature, journalism (political, economical, sports), and scientific divulgation.

Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk

Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed ‘variable penetrance’. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPR/Cas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants.Analysis of GTEx, cancer and autism data sets shows that cis-regulatory variation can modify the penetrance of coding variants. Deleterious coding variants on regulatory haplotypes resulting in high expression are enriched in disease cohorts and selected against in general populations.