CisCross: A gene list enrichment analysis to predict upstream regulators in Arabidopsis thaliana

Viktoriya V Lavrekha; Victor G Levitsky; Anton V Tsukanov; Anton G Bogomolov; Dmitry A Grigorovich; Nadya Omelyanchuk; Elena V Ubogoeva; Elena V Zemlyanskaya; Victoria Mironova

doi:10.3389/fpls.2022.942710

CisCross: A gene list enrichment analysis to predict upstream regulators in Arabidopsis thaliana

Front Plant Sci. 2022 Aug 18:13:942710. doi: 10.3389/fpls.2022.942710. eCollection 2022.

Authors

Affiliations

¹ Department of Systems Biology, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.
² Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia.
³ Department of Cell Biology, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.
⁴ Service of Information Technologies, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.
⁵ Department of Plant Systems Physiology, RIBES, Radboud University, Nijmegen, Netherlands.

Abstract

Having DNA-binding profiles for a sufficient number of genome-encoded transcription factors (TFs) opens up the perspectives for systematic evaluation of the upstream regulators for the gene lists. Plant Cistrome database, a large collection of TF binding profiles detected using the DAP-seq method, made it possible for Arabidopsis. Here we re-processed raw DAP-seq data with MACS2, the most popular peak caller that leads among other ones according to quality metrics. In the benchmarking study, we confirmed that the improved collection of TF binding profiles supported a more precise gene list enrichment procedure, and resulted in a more relevant ranking of potential upstream regulators. Moreover, we consistently recovered the TF binding profiles that were missing in the previous collection of DAP-seq peak sets. We developed the CisCross web service (https://plamorph.sysbio.ru/ciscross/) that gives more flexibility in the analysis of potential upstream TF regulators for Arabidopsis thaliana genes.

Keywords: DAP-seq; RNA-seq; multi-omics data integration; proximal promoters; transcription factor binding profiles.