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Transcriptome analysis using next generation sequencing (NGS) technology provides the capability to understand global changes in gene expression throughout a range of tissue samples. The nematode Caenorhabditis elegans (C. elegans) is a well-established genetic system used for analyzing a number of biological processes. C. elegans are a bacteria-eating soil nematode, and changes in bacterial diet have been shown to cause a number of physiological and molecular changes. Here we used Illumina RNA sequencing (RNA-seq) analysis to characterize the mRNA transcriptome of mixed C. elegans populations fed differing strains of bacteria to further understand dietary changes at the molecular level. Raw FASTQ files for the RNA-seq libraries are deposited in the NCBI Sequence Read Archive (SRA) and have been assigned BioProject accession PRJNA412551.
Pubmed ID: 31223636
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Software application that searches the BrainMap Database for papers of interest, reads their corresponding meta-data, and plots their results as coordinates on a standard glass brain in Talairach space.
View all literature mentionsSoftware Java pipeline for trimming tasks for Illumina paired end and single ended data. Flexible Trimmer for Illumina Sequence Data. Pair aware preprocessing tool optimized for Illumina next generation sequencing data. Includes several processing steps for read trimming and filtering. Operating systems Unix/Linux, Mac OS, Windows.
View all literature mentionsQuality control software that perform checks on raw sequence data coming from high throughput sequencing pipelines. This software also provides a modular set of analyses which can give a quick impression of the quality of the data prior to further analysis.
View all literature mentionsData aggregate that compiles results from bioinformatics analyses across multiple samples into a single report. It is written in Python.
View all literature mentionsSoftware tool for analysis of RNA-Seq experiments for which transcript abundances have been quantified with kallisto. Used for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance.
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