Vsevolod J. Makeev

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

EPIC challenges teams to predict transcription initiation from DNA sequence in non-model metazoans, aiming to assess generalizability beyond human and model species.

This study identifies DNA binding motifs for 98 human transcription factors using meSMiLE-seq, revealing methylation-sensitive binding preferences for some TFs.

The paper uses GHT-SELEX to show many human TFs have high intrinsic sequence specificity and complex DNA binding, often matching in vivo ChIP-seq results.

ArChIPelago combines multiple PWMs using classic machine learning to improve TFBS prediction, outperforming individual PWMs and other models across species.

The paper creates a promoterome and proteome atlas of human skeletal muscles, revealing molecular diversity and identifying key transcription factors and genetic variants associated with muscle traits.

The paper evaluates cross-platform DNA motif discovery tools using 4,237 experiments to identify reliable transcription factor binding motifs and highlights challenges in motif detection.

The paper identifies ATOH1 as a pro-metastatic oncogenic driver in SCLC by showing its role in cell survival and metastasis suppression upon depletion.

The paper introduces MIXALIME, a statistical framework for detecting allele-specific events in sequencing data, and demonstrates its application in constructing an atlas of allele-specific chromatin accessibility.

The paper identifies a new cancer-testis long noncoding RNA, OTP-AS1, which is antisense to the OTP gene and may regulate its expression in tumors.

The paper systematically determines the sequence specificity of 332 poorly characterized human transcription factors, identifying over 4,000 experiments and revealing tens of thousands of new TF binding sites.

The paper identifies an AH>GH substitution signature in mtDNA linked to aging and TSSS, showing higher frequency in species with longer generation lengths.

This study uses single-cell transcriptomics to show zebrafish pigment cells develop directly from broadly multipotent progenitors, challenging traditional fate restriction models.

The paper identifies allele-specific transcription factor binding events across the human genome using ChIP-Seq data, revealing regulatory variants linked to disease and molecular mechanisms.

The paper investigates mtDNA mutational spectra across species and tissues, finding a correlation between AH>GH substitutions and longevity, suggesting it as a potential oxidative damage signature in mtDNA.

The paper identifies cis-interacting lncRNAs and their targets using RNA-chromatin and Hi-C data across 18 human cell types.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.