Displaying 1 to 20 from 35 results

RNA-seq-analysis - RNAseq analysis notes from Ming Tang

  •    Python

Normalization is essential for RNAseq analysis. However, one needs to understand the underlining assumptions for each methods. Most methods assume there is no global changes between conditions (e.g. TMM normalization). However, this may not be true when global effect occurs. For example, if you delete a gene that controls transcription, you expect to see global gene expression reduction. In that case, other normalization methods need to be considered. (e.g. spike-in controls). The same principle applies to other high-throughput sequencing data such as ChIPseq. To estimate the library size, simply taking the total number of (mapped or unmapped) reads is, in our experience, not a good idea. Sometimes, a few very strongly expressed genes are differentially expressed, and as they make up a good part of the total counts, they skew this number. After you divide by total counts, these few strongly expressed genes become equal, and the whole rest looks differentially expressed.

degust - An interactive web-tool for RNA-seq analysis

  •    Javascript

View a short video of the interface in use. Read a summary on the Degust home page.

READemption - a pipeline for the computational evaluation of RNA-Seq data

  •    Python

READemption is a pipeline for the computational evaluation of RNA-Seq data. It was originally developed to process dRNA-Seq reads (as introduced by Sharma et al., Nature, 2010) originating from bacterial samples. Meanwhile is has been extended to process data generated in different experimental setups and from all domains of life. The functions which are accessible via a command-line interface cover read processing and aligning, coverage calculation, gene expression quantification, differential gene expression analysis as well as visualization. In order to set up and perform analyses quickly READemption follows the principal of "convention over configuration": Once the input files are copied/linked into defined folders no further parameters have to be given. Still, READemption's behavior can be adapted to specific needs of the user by parameters. Documentation can be found on here.

CD4-csaw - Reproducible reanalysis of a combined ChIP-Seq & RNA-Seq data set

  •    R

This is the code for a re-analysis of a GEO dataset that I originally analyzed for this paper using statistical methods that were not yet available at the time, such as the csaw Bioconductor package, which provides a principled way to normalize windowed counts of ChIP-Seq reads and test them for differential binding. The original paper only analyzed binding within pre-defined promoter regions. In addition, some improvements have also been made to the RNA-seq analysis using newer features of limma such as quality weights. This workflow downloads the sequence data and sample metadata from the public GEO/SRA release, so anyone can download and run this code to reproduce the full analysis.




picardmetrics - :vertical_traffic_light: Run Picard on BAM files and collate 90 metrics into one file

  •    Shell

Run Picard tools and collate multiple metrics files. Check the quality of your sequencing data. picardmetrics runs up to 12 Picard tools on each BAM file and collates all of the output files into a single table with up to 90 different metrics. It also automatically creates the .refFlat and .rRNA.list files required for CollectRnaSeqMetrics.

snakefiles - :snake: Snakefiles for common RNA-seq data analysis workflows.

  •    Python

This repository has Snakefiles for common RNA-seq data analysis workflows. Please feel free to copy them and modify them to suit your needs. If you are new to Snakemake, you might like to start by walking through my tutorial for beginners. Next, have a look at Johannes Koster's introductory slides, tutorial, documentation, and FAQ.

wiggleplotr - A small R package to make sequencing read coverage plots in R.

  •    R

wiggleplotr is a tool to visualise RNA-seq read overage overlapping gene annotations. A key feature of wiggleplotr is that it is able rescale all introns of a gene to fixed length, making it easier to see differences in read coverage between neighbouring exons that can otherwise be too far away. Since wiggleplotr takes standard BigWig files as input, it can also be used to visualise read overage from other sequencing-based assays such as ATAC-seq and ChIP-seq. However, the stable Bioconductor version is likely to be the best option for most people.

DGCA - Differential Gene Correlation Analysis

  •    HTML

The goal of DGCA is to calculate differential correlations across conditions. It simplifies the process of seeing whether two correlations are different without having to rely solely on parametric assumptions by leveraging non-parametric permutation tests and adjusting the resulting empirical p-values for multiple corrections using the qvalue R package.


poreplex - A versatile sequenced read processor for nanopore direct RNA sequencing

  •    Python

Signal-level preprocessor for Oxford Nanopore direct RNA sequencing (DRS) data. Poreplex does many preprocessing steps required before the downstream analyses for RNA Biology and yields the processed data in the ready-to-use forms. Poreplex requires Python 3.5+ and pip to install. This pip command installs poreplex with its essential dependencies. Currently, you need to install pomegranate manually before installing poreplex due to a memory leakage issue in the released versions of pomegranate. You may use the following command.

rnatoy - A proof of concept RNA-Seq pipeline with Nextflow

  •    Nextflow

A proof of concept of a RNA-Seq pipeline intended to show Nextflow scripting and reproducibility capabilities.

transabyss - de novo assembly of RNA-seq data using ABySS

  •    Python

Please use our Google Group for discussions and support. You may also create issues on our GitHub repository.

GGR-cwl - CWL tools and workflows for GGR

  •    Common

GGR pipelines created using the Common Workflow Language v1.0 specification. The workflows are parametrized with values that best suit the GGR samples, but they can be easily tailored for specific needs. For a detail User Guide to the CWL workflows, please see the wiki.

RNAseq-nf - RNAseq analysis pipeline

  •    HTML

Nextflow : for common installation procedures see the IARC-nf repository.

grape-nf - An automated RNA-seq pipeline using Nextflow

  •    Shell

Grape provides an extensive pipeline for RNA-Seq analyses. It allows the creation of an automated and integrated workflow to manage and analyse RNA-Seq data. It uses Nextflow as the execution backend. Please check Nextflow documentation for more information.

single-cell-pseudotime - An overview of algorithms for estimating pseudotime in single-cell RNA-seq data

  •    

Single cells, many algorithms. The goal of this page is to catalog the many algorithms that estimate pseudotimes for cells based on their gene expression levels. This problem is also referred to as single-cell trajectory inference or ordering. Ultimately, it will contain method names, software links, manuscript links, and brief comments on the particular strengths of each method. Initially, it seeks simply to list as many methods as possible. Some related methods not specifically designed for RNA-seq (e.g. mass cytometry) are included as well. The list also includes methods that are specifically designed to take pseudotemporal data as input. The initial list was created by Anthony Gitter, but pull requests are very welcome. Thank you to the other contributors.

irap - integrated RNA-seq Analysis Pipeline

  •    R

iRAP is a flexible RNA-seq analysis pipeline that allows the user to select and apply their preferred combination of existing tools for mapping reads, quantifying expression and testing for differential expression. Depending upon the application, iRAP can be used to quantify expression at the gene, exon or transcript level. Please consult the wiki (https://github.com/nunofonseca/irap/wiki) for further information.

HeatmapGenerator - Cross-platform GUI binary executable for making biological heatmaps

  •    C++

HeatmapGenerator is a graphical user interface software program written in C++, R, and OpenGL to create customized gene expression heatmaps from RNA-seq and microarray data in medical research. HeatmapGenerator can also be used to make heatmaps in a variety of other non-medical fields. HeatmapGenerator is peer-reviewed published software (http://www.scfbm.org/content/9/1/30). When using this software, please cite: [Khomtchouk et al.: “HeatmapGenerator: High performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline.” Source Code for Biology and Medicine, 2014 9:30].

Microscope - ChIP-seq/RNA-seq analysis software suite for gene expression heatmaps

  •    R

We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network visualization. MicroScope is financially supported by the United States Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program. This research was conducted with Government support under and awarded by DoD, Army Research Office (ARO), National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a.