A bird's-eye overview of molecular mechanisms regulating feed intake in chickens-with mammalian comparisons.

In recent decades, a lot of research has been conducted to explore poultry feeding behavior. However, up to now, the processes behind poultry feeding behavior remain poorly understood. The review generalizes modern expertise about the hormonal regulation of feeding behavior in chickens, focusing on signaling pathways mediated by insulin, leptin, and ghrelin and regulatory pathways with a cross-reference to mammals. This overview also summarizes state-of-the-art research devoted to hypothalamic neuropeptides that control feed intake and are prime candidates for predictors of feeding efficiency. Comparative analysis of the signaling pathways that mediate the feed intake regulation allowed us to conclude that there are major differences in the processes by which hormones influence specific neuropeptides and their contrasting roles in feed intake control between two vertebrate clades.

HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors.

We present a major update of the HOCOMOCO collection that provides DNA binding specificity patterns of 949 human transcription factors and 720 mouse orthologs. To make this release, we performed motif discovery in peak sets that originated from 14 183 ChIP-Seq experiments and reads from 2554 HT-SELEX experiments yielding more than 400 thousand candidate motifs. The candidate motifs were annotated according to their similarity to known motifs and the hierarchy of DNA-binding domains of the respective transcription factors. Next, the motifs underwent human expert curation to stratify distinct motif subtypes and remove non-informative patterns and common artifacts. Finally, the curated subset of 100 thousand motifs was supplied to the automated benchmarking to select the best-performing motifs for each transcription factor. The resulting HOCOMOCO v12 core collection contains 1443 verified position weight matrices, including distinct subtypes of DNA binding motifs for particular transcription factors. In addition to the core collection, HOCOMOCO v12 provides motif sets optimized for the recognition of binding sites in vivo and in vitro, and for annotation of regulatory sequence variants. HOCOMOCO is available at https://hocomoco12.autosome.org and https://hocomoco.autosome.org.

Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data.

Creating a complete picture of the regulation of transcription seems to be an urgent task of modern biology. Regulation of transcription is a complex process carried out by transcription factors (TFs) and auxiliary proteins. Over the past decade, ChIP-Seq has become the most common experimental technology studying genome-wide interactions between TFs and DNA. We assessed the transcriptional significance of cell line-specific features using regression analysis of ChIP-Seq datasets from the GTRD database and transcriptional start site (TSS) activities from the FANTOM5 expression atlas. For this purpose, we initially generated a large number of features that were defined as the presence or absence of TFs in different promoter regions around TSSs. Using feature selection and regression analysis, we identified sets of the most important TFs that affect expression activity of TSSs in human cell lines such as HepG2, K562 and HEK293. We demonstrated that some TFs can be classified as repressors and activators depending on their location relative to TSS.

Population size estimation for quality control of ChIP-Seq datasets.

Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets. Comprehensive control of dataset quality is currently indispensable to select the most reliable data for further analysis. In addition to existing quality control metrics, we have developed two novel metrics that allow to control false positives and false negatives in ChIP-Seq datasets. For this purpose, we have adapted well-known population size estimate for determination of unknown number of genuine transcription factor binding regions. Determination of the proposed metrics was based on overlapping distinct binding sites derived from processing one ChIP-Seq experiment by different peak callers. Moreover, the metrics also can be useful for assessing quality of datasets obtained from processing distinct ChIP-Seq experiments by a given peak caller. We also have shown that these metrics appear to be useful not only for dataset selection but also for comparison of peak callers and identification of site motifs based on ChIP-Seq datasets. The developed algorithm for determination of the false positive control metric and false negative control metric for ChIP-Seq datasets was implemented as a plugin for a BioUML platform: https://ict.biouml.org/bioumlweb/chipseq_analysis.html.

Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data.

OBJECTIVES: Mammalian genomics studies, especially those focusing on transcriptional regulation, require information on genomic locations of regulatory regions, particularly, transcription factor (TF) binding sites. There are plenty of published ChIP-Seq data on in vivo binding of transcription factors in different cell types and conditions. However, handling of thousands of separate data sets is often impractical and it is desirable to have a single global map of genomic regions potentially bound by a particular TF in any of studied cell types and conditions. DATA DESCRIPTION: Here we report human and mouse cistromes, the maps of genomic regions that are routinely identified as TF binding sites, organized by TF. We provide cistromes for 349 mouse and 599 human TFs. Given a TF, its cistrome regions are supported by evidence from several ChIP-Seq experiments or several computational tools, and, as an optional filter, contain occurrences of sequence motifs recognized by the TF. Using the cistrome, we provide an annotation of TF binding sites in the vicinity of human and mouse transcription start sites. This information is useful for selecting potential gene targets of transcription factors and detecting co-regulated genes in differential gene expression data.

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.

We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.

Assessment of translational importance of mammalian mRNA sequence features based on Ribo-Seq and mRNA-Seq data.

Ribosome profiling technology (Ribo-Seq) allowed to highlight more details of mRNA translation in cell and get additional information on importance of mRNA sequence features for this process. Application of translation inhibitors like harringtonine and cycloheximide along with mRNA-Seq technique helped to assess such important characteristic as translation efficiency. We assessed the translational importance of features of mRNA sequences with the help of statistical analysis of Ribo-Seq and mRNA-Seq data. Translationally important features known from literature as well as proposed by the authors were used in analysis. Such comparisons as protein coding versus non-coding RNAs and high- versus low-translated mRNAs were performed. We revealed a set of features that allowed to discriminate the compared categories of RNA. Significant relationships between mRNA features and efficiency of translation were also established.

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models.

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.