Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

Abstract

Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

Comparative transcriptomics of aphid species that diverged > 22 MYA reveals genes that are important for the maintenance of their symbiosis

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KDM6 demethylases mediate EWSR1-FLI1-driven oncogenic transformation in Ewing Sarcoma

“…We used SeqCode (Blanco et al, 2021b) for ChIP-seq downstream analysis across multiple stages: (i) the genome distribution of each set of peaks was generated by counting the number of peaks fitted on each class of region according to RefSeq annotations (O’Leary et al, 2016). Promoter is the region between 2.5 kb upstream and 2.5 kb downstream of the transcription start site (TSS).…”

A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters

“…Note: We will use the recoverChIPlevels function from SeqCode 12 to assign one value of signal strength (i.e., average count of reads) for every ChIP-seq experiment on each element of our collections of genomic regions.…”