ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs

Abstract

We present ANANASTRA, https://ananastra.autosome.org, a web server for the identification and annotation of regulatory single-nucleotide polymorphisms (SNPs) with allele-specific binding events. ANANASTRA accepts a list of dbSNP IDs or a VCF file and reports allele-specific binding (ASB) sites of particular transcription factors or in specific cell types, highlighting those with ASBs significantly enriched at SNPs in the query list. ANANASTRA is built on top of a systematic analysis of allelic imbalance in ChIP-Seq experiments and performs the ASB enrichment test against background sets of SNPs found in the same source experiments as ASB sites but not displaying significant allelic imbalance. We illustrate ANANASTRA usage with selected case studies and expect that ANANASTRA will help to conduct the follow-up of GWAS in terms of establishing functional hypotheses and designing experimental verification.

Statistical framework for calling allelic imbalance in high-throughput sequencing data

“…To showcase MIXALIME applicability to large-scale heterogeneous data, we performed ASE calling across the complete set of 5858 chromatin accessibility datasets uniformly reprocessed and available in GTRD ( Figure 5A, Supplementary Table S6 ) 72,73 . SNP calling was performed with GATK using read alignments from 1850/3801/207 DNase-/ATAC-/FAIRE-Seq experiments; we kept only sufficiently covered common heterozygous SNPs (0/1 in VCF GT field).…”

“…Next, we used ANANASTRA 72 to annotate these ASEs with ASBs, which resulted in 39 intersected SNPs (61%). Among transcription factors that bind these ASBs, ZFX, Zinc Finger X-Chromosomal Protein, turned out to be the most common with seven target ASBs, three of which (rs11730091, rs131804, rs28418438) were concordant with its binding motif.…”

Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases

“…Boytsov et al . [ 68 ] recently developed ANANASTRA, an upgraded version of ADASTRA [ 69 ], a web server that can accurately predict allele-specific binding events of TFs in different cell types [ 68 ]. This program requires inputs from four databases: allele-specific binding events from GTRD (ChIP-seq data) [ 70 ], binding patterns from HOCOMOCO (TF motif predictions) [ 71 ], a list of variants from dbSNP (rs-IDs) [ 48 ], and tissue-specific context from the GTEx project (eQTL) [ 72 ].…”

“…We recommend incorporating multi-omics and functional genomics datasets (genomic, transcriptomic, epigenomic, etc. ), which can improve the predictive power of the computational models to identify variants with a temporal- or tissue-specific impact [ 68 , 91 , 111 , 121 , 122 ]. In our previous work on cardiac TFs, we implemented predictive models (PWM- and SVM-based) to prioritize cardiovascular disease (CVD)-associated SNVs from the GWAS catalog [ 33 , 75 ].…”

Interpreting non-coding genome variation with DNA sequence motifs

“…With this toolkit, we assembled the wellknown HOCOMOCO [3] collection of sequence motifs recognized by human TFs and used it to annotate single-nucleotide variants linked with autoimmune diseases. On top of that, we show how advanced statistics backed up with motif analysis allows for mapping causative allele-specific regulatory elements (ADASTRA [6] and ANANASTRA [7]), and discuss surprising pitfalls and peculiarities of machine learning applications in the analysis of regulatory variants [8]. Acknowledgements: Russian Science Foundation (20-74-10075 to I.V.K.…”