SUMMARY: Public repositories such as NCBI's Gene Expression Omnibus (GEO) contain large numbers of ChIP-seq experiments, but their reuse is limited by heterogeneous free-text metadata describing target proteins, histone marks, cell lines, tissues, and disease states. We introduce CistromeMeta, a Python-based command-line tool that leverages large language models (LLMs) in a few-shot setting to automatically extract and standardize ChIP-seq metadata from GEO XML records without custom model training. The tool validates extracted terms against authoritative biological databases, including NCBI Gene, Harmonizome 3.0, AnimalTFDB 4.0, Cellosaurus, Experimental Factor Ontology, and Uberon, producing standardized outputs with official gene symbols and ontology identifiers for scalable metadata curation. AVAILABILITY AND IMPLEMENTATION: The Python source code is freely available at https://github.com/nickpiccaro/CistromeMetaX. An archived version of the software is available through Zenodo at DOI: 10.5281/zenodo.20244834. The tool requires Python 3.6+ and an OpenAI API key.

SUMMARY: The regulation of genes by cis-regulatory elements (CREs) is complex and differs between cell types. Visual analysis of large collections of chromatin profiles across diverse cell types, integrated with computational methods, can reveal meaningful biological insights. We developed Cistrome Explorer, a web-based interactive visual analytics tool for exploring thousands of chromatin profiles in diverse cell types. Integrated with the Cistrome Data Browser database which contains thousands of ChIP-seq, DNase-seq and ATAC-seq samples, Cistrome Explorer enables the discovery of patterns of CREs across cell types and the identification of transcription factor binding underlying these patterns. AVAILABILITY AND IMPLEMENTATION: Cistrome Explorer and its source code are available at http://cisvis.gehlenborglab.org/ and released under the MIT License. Documentation can be accessed via http://cisvis.gehlenborglab.org/docs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Genome-wide clustered, regularly interspaced, short palindromic repeat (CRISPR)-Cas9 screen has been widely used to interrogate gene functions. However, the rules to design better libraries beg further refinement. Results: We found single guide RNA (sgRNA) outliers are characterized by higher G-nucleotide counts, especially in regions distal from the PAM motif and are associated with stronger off-target activities. Furthermore, using non-targeting sgRNAs as negative controls lead to strong bias, which can be mitigated by using sgRNAs targeting multiple 'safe harbor' regions. Custom-designed screens confirmed our findings and further revealed that 19 nt sgRNAs consistently gave the best signal-to-noise ratio. Collectively, our analysis motivated the design of a new genome-wide CRISPR/Cas9 screen library and uncovered some intriguing properties of the CRISPR-Cas9 system. Availability and implementation: The MAGeCK workflow is available open source at https://bitbucket.org/liulab/mageck_nest under the MIT license. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Transcription factor binding, histone modification, and chromatin accessibility studies are important approaches to understanding the biology of gene regulation. ChIP-seq and DNase-seq have become the standard techniques for studying protein-DNA interactions and chromatin accessibility respectively, and comprehensive quality control (QC) and analysis tools are critical to extracting the most value from these assay types. Although many analysis and QC tools have been reported, few combine ChIP-seq and DNase-seq data analysis and quality control in a unified framework with a comprehensive and unbiased reference of data quality metrics. RESULTS: ChiLin is a computational pipeline that automates the quality control and data analyses of ChIP-seq and DNase-seq data. It is developed using a flexible and modular software framework that can be easily extended and modified. ChiLin is ideal for batch processing of many datasets and is well suited for large collaborative projects involving ChIP-seq and DNase-seq from different designs. ChiLin generates comprehensive quality control reports that include comparisons with historical data derived from over 23,677 public ChIP-seq and DNase-seq samples (11,265 datasets) from eight literature-based classified categories. To the best of our knowledge, this atlas represents the most comprehensive ChIP-seq and DNase-seq related quality metric resource currently available. These historical metrics provide useful heuristic quality references for experiment across all commonly used assay types. Using representative datasets, we demonstrate the versatility of the pipeline by applying it to different assay types of ChIP-seq data. The pipeline software is available open source at https://github.com/cfce/chilin . CONCLUSION: ChiLin is a scalable and powerful tool to process large batches of ChIP-seq and DNase-seq datasets. The analysis output and quality metrics have been structured into user-friendly directories and reports. We have successfully compiled 23,677 profiles into a comprehensive quality atlas with fine classification for users.

MOTIVATION: RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single replicate studies, there is currently no satisfactory method for detecting differentially expressed genes when only a single biological replicate is available. RESULTS: We present the GFOLD (generalized fold change) algorithm to produce biologically meaningful rankings of differentially expressed genes from RNA-seq data. GFOLD assigns reliable statistics for expression changes based on the posterior distribution of log fold change. In this way, GFOLD overcomes the shortcomings of P-value and fold change calculated by existing RNA-seq analysis methods and gives more stable and biological meaningful gene rankings when only a single biological replicate is available. AVAILABILITY: The open source C/C++ program is available at http://www.tongji.edu.cn/∼zhanglab/GFOLD/index.html

UNLABELLED: Transcription factor binding events are frequently associated with a pattern of nucleosome occupancy changes in which nucleosomes flanking the binding site increase in occupancy, while those in the vicinity of the binding site itself are displaced. Genome-wide information on enhancer proximal nucleosome occupancy can be readily acquired using ChIP-seq targeting enhancer-related histone modifications such as H3K4me2. Here, we present a software package, BINOCh that allows biologists to use such data to infer the identity of key transcription factors that regulate the response of a cell to a stimulus or determine a program of differentiation. AVAILABILITY: The BINOCh open source Python package is freely available at http://liulab.dfci.harvard.edu/BINOCh under the FreeBSD license.