Literature Hub
文献与动态集
将经典方法论文与平台内的分析流程、软件算法和数据库教程相互关联，形成可追溯的证据链。
10
papers
6
linked
Dictionary learning for integrative, multimodal and scalable single-cell analysis
This work presents dictionary learning strategies used in Seurat v5 for integrative and scalable analysis of multimodal single-cell data. The study connects computational representation learning with practical single-cell tasks such as cross-dataset integration, modality alignment and annotation transfer across complex biological atlases.
CUT&Tag for efficient epigenomic profiling of small samples and single cells
This paper describes CUT&Tag, an enzyme-tethering approach for highly efficient and sensitive epigenomic profiling from small samples and single cells. The method uses protein-A-Tn5 transposase fusion targeted by specific antibodies to perform tagmentation directly on chromatin in permeabilized cells or nuclei, eliminating the need for traditional ChIP-seq library preparation steps.
Massively parallel digital transcriptional profiling of single cells
This paper describes a high-throughput droplet-based single-cell RNA-seq system that enabled large-scale digital transcriptional profiling. It is a key experimental foundation for 10x Genomics workflows and motivates the preprocessing steps performed by tools such as Cell Ranger.
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
This landmark study introduced spatial transcriptomics, a method that enables visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By placing tissue sections on arrayed reverse transcription primers with unique positional barcodes, the method captures both global gene expression patterns and the spatial organization of the tissue microenvironment.
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
This paper describes featureCounts, a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoters, and genomic bins. The method implements highly efficient chromosome hashing and feature blocking techniques to achieve read counting speeds substantially faster than existing methods while maintaining accuracy.
STAR: ultrafast universal RNA-seq aligner
This paper introduces STAR (Spliced Transcripts Alignment to a Reference), an ultrafast RNA-seq aligner that achieves high alignment accuracy by using uncompressed suffix arrays together with a novel sequential maximum mappable seed search strategy. STAR can align both short and long reads at speeds an order of magnitude faster than contemporary tools while maintaining splice junction discovery capabilities.
clusterProfiler: an R package for comparing biological themes among gene clusters
This paper presents clusterProfiler, an R package that implements methods for analyzing and visualizing functional profiles of gene clusters. The package supports over-representation analysis and gene set enrichment analysis of Gene Ontology and KEGG pathways, providing statistical analysis and rich visualization for interpreting functional enrichment results from high-throughput experiments.
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia
This comprehensive paper provides guidelines and best practices for ChIP-seq experiments as developed by the ENCODE and modENCODE consortia. It addresses antibody validation, library complexity, sequencing depth, replication, and data analysis standards that have become foundational references for the chromatin biology community.
Fast and accurate short read alignment with Burrows-Wheeler transform
This landmark paper introduced BWA, a Burrows-Wheeler transform based short-read aligner designed for efficient mapping against large reference genomes. It established the algorithmic foundation for many WGS and WES variant calling pipelines, including workflows that later adopted BWA-MEM for longer reads and paired-end sequencing data.
WGCNA: an R package for weighted correlation network analysis
This paper presents the WGCNA R package, a comprehensive collection of functions for performing weighted gene co-expression network analysis. WGCNA identifies gene modules based on pairwise correlations, relates modules to external sample traits, and calculates module membership measures. It has become an essential tool for systems biology and co-expression network studies.