Biology subjects

Cordero, F.

Publications and source records attributed to Cordero, F..

3 recordsLinked to original sources

bioRxiv2018

rCASC: reproducible Classification Analysis of Single Cell sequencing data

SummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.\n\nAvailability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.\n\nSupplementary informationSupplementary data are available at rCASC github

bioinformatics↗

bioRxiv2018

Complementary information on single nucleotide variants, INDELs and functional translocations can be obtained with RNAseq using different library preparations

BackgroundRNA-seq represents an attractive methodology for the detection of functional genomic variants because it allows the integration of variant frequency and their expression. However, although specific statistic frameworks have been designed to detect SNVs/INDELS/gene fusions in RNA-seq data, very little has been done to understand the effect of library preparation protocols on transcript variant detection in RNA-seq data.\n\nResultsHere, we compared RNA-seq results obtained on short reads sequencing platform with two protocols: one based on polyA+ RNA selection protocol (POLYA) and the other based on exonic regions capturing protocol (ACCESS). Our data indicate that ACCESS detects 10% more coding SNV/INDELs with respect to POLYA, making this protocol more suitable for this goal. Furthermore, ACCESS requires less reads for coding SNV detection with respect to POLYA. On the other hand, if the analysis aims at identifying SNV/INDELs also in the 5and 3 UTRs, POLYA is definitively the preferred method. No particular advantage comes from the usage of ACCESS or POLYA in the detection of fusion transcripts.\n\nConclusionData show that a careful selection of the \"wet\" protocol adds specific features that cannot be obtained with bioinformatics alone.

genomics↗

bioRxiv2017

Reproducible Bioinformatics Project: A community for reproducible bioinformatics analysis pipelines

BackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and a list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.\n\nResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.\n\nConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

bioinformatics↗