10/10
The FANTOMUS atlas is available at fantomus.autosome.org: an interactive browser of skeletal muscles’ gene expression, protein abundance, TRE expression, and motif activity.
Read more: www.biorxiv.org/content/10.6...

9/10
Finally, allele-specific analysis revealed 6653 single-nucleotide variants with a significant allelic imbalance, which were enriched by GTEx eQTLs, ADASTRA ASBs, and associated with total thigh muscle volume.

8/10
To identify the key regulators driving the observed muscle heterogeneity, we performed motif activity response analysis. 353 motifs have non-uniform activity across muscles, including MEF2, SIX, and HSF proteins, and eye-specific ZBTB7A.

7/10
Besides gene expression, CAGE-Seq revealed alternative promoter usage: for 1648 TREs of 1264 genes, including TPM3 and PITX2, the contribution to the total gene expression was significantly varying across muscles.

6/10
Non-uniformly expressed genes include sarcomere components, HOX transcription factors, and atrophy- and dystrophy-associated genes, e.g., FKRP, reflecting the differences in the tissue etiology, fiber contraction, and varying susceptibility to myopathies.

5/10
Surprisingly, more than 80% of genes and proteins demonstrated significant differential expression across muscles. Extraocular muscles, tongue, and diaphragm have the most uncommon activity patterns of promoters and enhancers.

4/10
Further, with mass spectrometry, we assessed the abundance of 1804 protein groups encompassing 1895 proteins, and estimated the concordance of transcriptomic and proteomic readouts.

3/10
Inspired by FANTOM5 (fantom.gsc.riken.jp/5/), FANTOMUS took advantage of CAGE-Seq to identify and measure the activity of 37001 TREs, including promoters of 18329 genes.

2/10
FANTOMUS delineates activity of transcribed regulatory elements (TREs = promoters + transcribed enhancers) and protein abundance profiles in 75 (CAGE-Seq) and 22 (LC-MS/MS) distinct human skeletal muscles.

1/10
Did you know that molecular phenotypes of human skeletal muscles are vastly different? Meet FANTOMUS, the skeletal muscles promoterome-proteome atlas:
doi.org/10.64898/202...
made with
@andreybuyan.bsky.social @nikitagryzunov.bsky.social @alforrest.bsky.social @sevamakeev.bsky.social

(1/13) Excited to share the outcome of the IBIS Challenge! The IBIS challenge united dozens of teams across the world in tackling the problem of modeling transcription factor (TF) binding specificity using a diverse collection of experimental datasets for understudied human TFs.

Our paper on LARGE-scale benchmarking of motif discovery tools is published! nature.com/articles/s42...

It was a long, 7 years long journey, which coordinated efforts of 50+ researchers, proud to be on of them.

More results from Codebook about poorly studied TFs are coming soon.

(15/15) MIXALIME is written in Python, getting the stable version is as easy as 'pip install mixalime', and the source code plus tutorial are freely available at github.com/autosome-ru/...

(14/15) We hope you will find MIXALIME and UDACHA useful in your study of regulatory sequence variants.

(13/15) According to stratified LD score regression, these rSNPs correspond to regulatory regions involved in cell type-specific phenotypes. Most importantly, the collection of significant rSNPs can be fully explored at udacha.autosome.org

(12/15) AS-chromatin variants are predominantly located in promoter and enhancer regions and significantly overlap ADASTRA ASBs and GTEx eQTLs.

(11/15) Finally, we used MIXALIME to analyze 5858 chromatin accessibility datasets from gtrd.biouml.org. In the end, we identified >200 thousand allele-specific chromatin accessibility variants.

(10/15) In most cases, MIXALIME outperforms other popular tools for AS calling, offering a good sensitivity/specificity tradeoff.

(9/15) Using heart CAGE-Seq data from Deviatiiarov et al., we benchmarked MIXALIME by calling AS variants in CAGE-Seq and comparing them to allele-specific transcription factor binding from ADASTRA and eQTLs from GTEx.

(8/15) Copy-number variation and aneuploidy are accounted for by fitting a mixture model assuming that reads originate from haplotypes with different copy numbers.

(7/15) MIXALIME also handles reference mapping bias and aneuploidy, see the underlying math in arxiv.org/abs/2306.08287. To counter the mapping bias, MIXALIME uses separate fits for Alt read counts with the fixed number of Ref reads and vice versa.

(6/15) MIXALIME provides a variety of statistical models to fulfill particular use cases, from a standard binomial model to the beta negative binomial (BetaNB) model that accounts for extra overdispersion.

(5/15) MIXALIME is a novel toolbox that uses MIXture models for ALlelic IMbalance Estimation. In the paper, we describe a general workflow from FASTQ files to allelic read counts and SNV-level allele-specific statistics.

(4/15) Technically, the allele specificity is revealed by counting the number of reads supporting each of the alleles and estimating the statistical significance of the observed allelic imbalance.

(3/15) High-throughput sequencing allows tracking chromatin state, gene expression, protein-DNA interactions, and more. Eventually, all methods yield short reads that can be used to call single-nucleotide variants and assess the allele specificity.

(2/15) Check the original Tweetorial describing the respective preprint x.com/halfacrocodi... or follow the thread below.

(1/15) Yet another sweet bioinformatics "software+database" couple from our team:
Meet MIXALIME, a framework for assessing allelic imbalance, and UDACHA, a database of allele-specific chromatin accessibility, read more at www.nature.com/articles/s41...

Last but not least: this update became possible thanks to the experimental data & motif analysis performed within the Codebook/GRECO-BIT collaboration, ibis.autosome.org/docs/about_us

(4/4) Don't hesitate to grab a fresh release from hocomoco.autosome.org and remember that we also provide a fancy online motif scanner, MoLoTool, in all its interactive JS-powered beauty.

(3/4) Also do not forget that HOCOMOCO also provides motifs for orthologous mouse TFs, >800 of those are covered in v13.