On the identifiability of the isoform deconvolution problem: application to select the proper fragment length in an RNA-seq library.

MOTIVATION: Isoform deconvolution is an NP-hard problem. The accuracy of the proposed solutions is far from perfect. At present, it is not known if gene structure and isoform concentration can be uniquely inferred given paired-end reads, and there is no objective method to select the fragment length to improve the number of identifiable genes. Different pieces of evidence suggest that the optimal fragment length is gene-dependent, stressing the need for a method that selects the fragment length according to a reasonable trade-off across all the genes in the whole genome. RESULTS: A gene is considered to be identifiable if it is possible to get both the structure and concentration of its transcripts univocally. Here, we present a method to state the identifiability of this deconvolution problem. Assuming a given transcriptome and that the coverage is sufficient to interrogate all junction reads of the transcripts, this method states whether or not a gene is identifiable given the read length and fragment length distribution. Applying this method using different read and fragment length combinations, the optimal average fragment length for the human transcriptome is around 400-600 nt for coding genes and 150-200 nt for long non-coding RNAs. The optimal read length is the largest one that fits in the fragment length. It is also discussed the potential profit of combining several libraries to reconstruct the transcriptome. Combining two libraries of very different fragment lengths results in a significant improvement in gene identifiability. AVAILABILITY AND IMPLEMENTATION: Code is available in GitHub (https://github.com/JFerrer-B/transcriptome-identifiability). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A universal SNP and small-indel variant caller using deep neural networks.

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.

Ellagic acid antagonizes Bnip3-mediated mitochondrial injury and necrotic cell death of cardiac myocytes.

The Bcl-2 protein Bnip3 is crucial for provoking oxidative injury to mitochondria following anthracycline treatment or ischemia-reperfusion injury. Herein, we investigate the effects of the polyphenolic compound ellagic acid (EA) on Bnip3 mediated mitochondrial injury and necrotic cell death in cardiac myocytes. In contrast to vehicle treated cardiomyocytes, Bnip3 was highly enriched in mitochondrial fractions of cardiac myocytes treated with the anthracycline doxorubicin or in cells subjected to hypoxia (HPX). Mitochondrial associated Bnip3 was accompanied by mPTP opening and loss of ∆Ψm. The dynamin related fission protein Drp-1 was phosphorylated (Drp1616) and coincided with excessive mitochondrial fragmentation, mitophagy and necrosis in cardiac myocytes treated with doxorubicin or subjected to hypoxia. Moreover, knock-down of Bnip3 was sufficient to prevent mitochondrial fission and doxorubicin-induced cell death supporting the involvement of Bnip3 in doxorubicin cardiotoxity. Interestingly, mitochondrial associated Bnip3 in cells treated with doxorubicin was markedly reduced by EA. This resulted in significantly less mitochondrial fission and cell death. Notably, EA similarly suppressed mitochondrial injury and cell death induced by hypoxia or Bnip3 over-expression. Herein, we identify a novel signaling axis that operationally links EA and Bnip3 for suppression of cardiac cell death. We provide compelling new evidence that EA suppresses mitochondrial injury and necrotic cell death of cardiac myocytes by functionally abrogating Bnip3 activity. Hence, by suppressing mitochondrial injury induced by Bnip3, EA may provide a therapeutic advantage in reducing oxidative injury and cardiac dysfunction in cancer patients undergoing anthracycline treatment or individuals with ischemic cardiac stress.

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis.

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.RNA-seq is widely used for transcriptome analysis. Here, the authors analyse a wide spectrum of RNA-seq workflows and present a comprehensive analysis protocol named RNACocktail as well as a computational pipeline leveraging the widely used tools for accurate RNA-seq analysis.

COSINE: non-seeding method for mapping long noisy sequences.

Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short k-mers (k = 3-4) along the sequences. The results on simulated and real data show that COSINE achieves high sensitivity and specificity under a wide range of read accuracies. When the error rate is high, COSINE can offer substantial advantages over existing alignment methods.

Overexpression of Human SOD1 Leads to Discrete Defects in the Cerebellar Architecture in the Mouse.

The human superoxide dismutase 1 (SOD1) gene is responsible for neutralizing supercharged oxygen radicals within the cell. Mutation in SOD1 gene causes amyotrophic lateral sclerosis (ALS). Recent studies have shown involvement of the cerebellum in ALS, although the cerebellar contribution in SOD1 transgenic mice remains unclear. Using immunohistopathology, we investigated the Purkinje cell phenotype in the vermis of the SOD1 transgenic mice cerebellum. Calbindin 1 (Calb1) and three well-known zone and stripe markers, zebrin II, HSP25, and PLCß4 have been used to explore possible alteration in zone and stripe. Here we show that Calb1 expression is significantly reduced in a subset of the Purkinje cells that is almost aligned with the cerebellar zones and stripes pattern. The Purkinje cells of SOD1 transgenic mice display a pattern of Calb1 down-regulation, which seems to proceed to Purkinje cell degeneration as the mice age. The onset of Calb1 down-regulation in Purkinje cells begins from the central zone and continues into the nodular zone, however it has not been observed in the anterior and posterior zones. In a subgroup of SOD1 transgenic mice in which gait unsteadiness was apparent, down-regulation of Calb1 is seen in a subset of PLCß4+ Purkinje cells in the anterior zone. These observations suggest that the Calb1- subset of Purkinje cells in the anterior zone, which receives somatosensory input, causes unsteady gait. Our data suggest that human SOD1 overexpression leads to Calb1 down-regulation in the zone and strip pattern and raise the question of whether SOD1 overexpression leads to Purkinje cells degeneration.

An ensemble approach to accurately detect somatic mutations using SomaticSeq.

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.

A high-confidence, comprehensive human variant set is critical in assessing accuracy of sequencing algorithms, which are crucial in precision medicine based on high-throughput sequencing. Although recent works have attempted to provide such a resource, they still do not encompass all major types of variants including structural variants (SVs). Thus, we leveraged the massive high-quality Sanger sequences from the HuRef genome to construct by far the most comprehensive gold set of a single individual, which was cross validated with deep Illumina sequencing, population datasets, and well-established algorithms. It was a necessary effort to completely reanalyze the HuRef genome as its previously published variants were mostly reported five years ago, suffering from compatibility, organization, and accuracy issues that prevent their direct use in benchmarking. Our extensive analysis and validation resulted in a gold set with high specificity and sensitivity. In contrast to the current gold sets of the NA12878 or HS1011 genomes, our gold set is the first that includes small variants, deletion SVs and insertion SVs up to a hundred thousand base-pairs. We demonstrate the utility of our HuRef gold set to benchmark several published SV detection tools.

Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing.

We developed an innovative hybrid sequencing approach, IDP-fusion, to detect fusion genes, determine fusion sites and identify and quantify fusion isoforms. IDP-fusion is the first method to study gene fusion events by integrating Third Generation Sequencing long reads and Second Generation Sequencing short reads. We applied IDP-fusion to PacBio data and Illumina data from the MCF-7 breast cancer cells. Compared with the existing tools, IDP-fusion detects fusion genes at higher precision and a very low false positive rate. The results show that IDP-fusion will be useful for unraveling the complexity of multiple fusion splices and fusion isoforms within tumorigenesis-relevant fusion genes.

Characterization of the human ESC transcriptome by hybrid sequencing.

Although transcriptional and posttranscriptional events are detected in RNA-Seq data from second-generation sequencing, full-length mRNA isoforms are not captured. On the other hand, third-generation sequencing, which yields much longer reads, has current limitations of lower raw accuracy and throughput. Here, we combine second-generation sequencing and third-generation sequencing with a custom-designed method for isoform identification and quantification to generate a high-confidence isoform dataset for human embryonic stem cells (hESCs). We report 8,084 RefSeq-annotated isoforms detected as full-length and an additional 5,459 isoforms predicted through statistical inference. Over one-third of these are novel isoforms, including 273 RNAs from gene loci that have not previously been identified. Further characterization of the novel loci indicates that a subset is expressed in pluripotent cells but not in diverse fetal and adult tissues; moreover, their reduced expression perturbs the network of pluripotency-associated genes. Results suggest that gene identification, even in well-characterized human cell lines and tissues, is likely far from complete.