Renan Valieris

Bio: Renan Valieris is an academic researcher from AC Camargo Hospital. The author has contributed to research in topics: Exome sequencing & Cancer. The author has an hindex of 10, co-authored 25 publications receiving 917 citations.

Bioconda: sustainable and comprehensive software distribution for the life sciences.

Abstract: We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.

Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs.

Abstract: In humans, conventional dendritic cells (cDCs) exist as two unique populations characterized by expression of CD1c and CD141. cDCs arise from increasingly restricted but well-defined bone marrow progenitors that include the common DC progenitor that differentiates into the pre-cDC, which is the direct precursor of cDCs. In this study, we show that pre-cDCs in humans are heterogeneous, consisting of two distinct populations of precursors that are precommitted to become either CD1c+ or CD141+ cDCs. The two groups of lineage-primed precursors can be distinguished based on differential expression of CD172a. Both subpopulations of pre-cDCs arise in the adult bone marrow and can be found in cord blood and adult peripheral blood. Gene expression analysis revealed that CD172a+ and CD172a− pre-cDCs represent developmentally discrete populations that differentially express lineage-restricted transcription factors. A clinical trial of Flt3L injection revealed that this cytokine increases the number of both CD172a− and CD172a+ pre-cDCs in human peripheral blood.

Clonal CD4+ T cells in the HIV-1 latent reservoir display a distinct gene profile upon reactivation.

Abstract: Despite suppressive combination antiretroviral therapy (ART), latent HIV-1 proviruses persist in patients. This latent reservoir is established within 48–72 h after infection, has a long half-life1,2, enables viral rebound when ART is interrupted, and is the major barrier to a cure for HIV-13. Latent cells are exceedingly rare in blood (∼1 per 1 × 106 CD4+ T cells) and are typically enumerated by indirect means, such as viral outgrowth assays4,5. We report a new strategy to purify and characterize single reactivated latent cells from HIV-1-infected individuals on suppressive ART. Surface expression of viral envelope protein was used to enrich reactivated latent T cells producing HIV RNA, and single-cell analysis was performed to identify intact virus. Reactivated latent cells produce full-length viruses that are identical to those found in viral outgrowth cultures and represent clones of in vivo expanded T cells, as determined by their T cell receptor sequence. Gene-expression analysis revealed that these cells share a transcriptional profile that includes expression of genes implicated in silencing the virus. We conclude that reactivated latent T cells isolated from blood can share a gene-expression program that allows for cell division without activation of the cell death pathways that are normally triggered by HIV-1 replication. A shared gene expression program associated with silencing HIV-1 transcription may be critical for persistence of reactivated latent CD4+ T cells in patients with HIV.

signeR: an empirical Bayesian approach to mutational signature discovery.

Abstract: Motivation Mutational signatures can be used to understand cancer origins and provide a unique opportunity to group tumor types that share the same origins and result from similar processes. These signatures have been identified from high throughput sequencing data generated from cancer genomes by using non-negative matrix factorisation (NMF) techniques. Current methods based on optimization techniques are strongly sensitive to initial conditions due to high dimensionality and nonconvexity of the NMF paradigm. In this context, an important question consists in the determination of the actual number of signatures that best represent the data. The extraction of mutational signatures from high-throughput data still remains a daunting task. Results Here we present a new method for the statistical estimation of mutational signatures based on an empirical Bayesian treatment of the NMF model. While requiring minimal intervention from the user, our method addresses the determination of the number of signatures directly as a model selection problem. In addition, we introduce two new concepts of significant clinical relevance for evaluating the mutational profile. The advantages brought by our approach are shown by the analysis of real and synthetic data. The later is used to compare our approach against two alternative methods mostly used in the literature and with the same NMF parametrization as the one considered here. Our approach is robust to initial conditions and more accurate than competing alternatives. It also estimates the correct number of signatures even when other methods fail. Results on real data agree well with current knowledge. Availability and implementation signeR is implemented in R and C ++, and is available as a R package at http://bioconductor.org/packages/signeR CONTACT: itojal@cipe.accamargo.org.brSupplementary information: Supplementary data are available at Bioinformatics online.

A total transcriptome profiling method for plasma-derived extracellular vesicles: applications for liquid biopsies

Abstract: Extracellular vesicles (EVs) are key mediators of intercellular communication. Part of their biological effects can be attributed to the transfer of cargos of diverse types of RNAs, which are promising diagnostic and prognostic biomarkers. EVs found in human biofluids are a valuable source for the development of minimally invasive assays. However, the total transcriptional landscape of EVs is still largely unknown. Here we develop a new method for total transcriptome profiling of plasma-derived EVs by next generation sequencing (NGS) from limited quantities of patient-derived clinical samples, which enables the unbiased characterization of the complete RNA cargo, including both small- and long-RNAs, in a single library preparation step. This approach was applied to RNA extracted from EVs isolated by ultracentrifugation from the plasma of five healthy volunteers. Among the most abundant RNAs identified we found small RNAs such as tRNAs, miRNAs and miscellaneous RNAs, which have largely unknown functions. We also identified protein-coding and long noncoding transcripts, as well as circular RNA species that were also experimentally validated. This method enables, for the first time, the full spectrum of transcriptome data to be obtained from minute patient-derived samples, and will therefore potentially allow the identification of cell-to-cell communication mechanisms and biomarkers.

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

Abstract: The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

Twelve years of SAMtools and BCFtools.

Abstract: Background: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.

The Repertoire of Mutational Signatures in Human Cancer

Abstract: Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3–15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer. The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.