CSBB-v2.0 is released and available for download

Download CSBB-v2.0 for free! Computational Suite for Bioinformaticians and Biologists https://sourceforge.net/projects/csbb-v2-0/

What’s New:

1) Install module for Linux and MAC users → this module will auto install all the required dependencies for running RNA-SEQ pipelines using CSBB

2) Ability to process RNA-SEQ data [Single and Paired End]

3) Ability to generate TPM and Counts matrices for both Isoforms and Genes

4) Updates to existing Modules

5) RNA-SEQ pipelines workflow for human [hg19] and mouse [mm10]

6) RNA-SEQ pipelines for other genomes can be added on request from users

7) Auto download and update for R packages

8) Bug Fixes * Version2 addition/updates only for MacOS and LINUX * RNA-SEQ pipeline does not work for windows currently *

Information on Modules: Install : This module helps in auto installing all the required dependencies for running RNA-SEQ modules through CSBB. This module installs samtools, bowtie2, RSEM, bedtools, wget and fastqc.

Install module run is required as a one-time process before attempting RNA-SEQ process modules of CSBB.

1) UpperQuantile: Upper quantile normalization is used to normalize two or more distributions in absence of a reference distribution. Given a matrix of size M x N, where M is the number of rows and N is the number of columns, UpperQuantile will determine the 75th quantile value for ith column and normalize it across ith column. Upper quantile normalization is regularly used to remove variation from global gene expression data from microarrays and next generation sequencing data.

2) BasicStats: This module is designed for some basic and quick statistical calculations on gene expression data across samples. BasicStats calculates mean, median, median adjusted deviation, standard deviation, variance, minimum and maximum for each gene based on expression across samples. Assume, you have m*n matrix where m are rows (genes) and n are columns (samples). BasicStats will calculated above mentioned statistics for each row (gene).

3) ExpressionToZscore: This module converts expression values to z-scores. This module is helpful when a user wants to compare expression data from different sources (labs or platforms). This module converts expression value of a gene in each sample to z-score based on expression across the samples. Let’s say there is a m*n expression matrix then each value in the matrix will be converted to z-score based on values for each row across columns.

4) ExtractGeneInfo: This module helps to extract information/expression for a list of genes from a large matrix of genes and their expression/information across samples/columns.

(learn more…)

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