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We present MUSIC, a signal processing approach for identification of enriched regions in ChIP-Seq data, available at http://​www.​music.​gersteinlab.​org. MUSIC ...
To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account ...
Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression.
The broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a...
To mitigate these effects, we select female gametic cells that are developmentally stable and void of endoreduplication. Using single-cell RNA sequencing (scRNA-seq) in Arabidopsis thaliana tetraploid lines and i...
CRISPR screens with single-cell transcriptomic readouts are a valuable tool to understand the effect of genetic perturbations including single nucleotide variants (SNVs) associated with diseases. Interpretation o...
We perform a quantitative RNA-seq analysis of embryo sacs, comparator ovules with...de novo assembly of RNA-seq reads. Transposon-related transcripts are present in...
Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptom...
Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While op...
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at...
Although comparison of RNA-protein interaction profiles across different conditions has become increasingly important to understanding the function of RNA-binding proteins (RBPs), few computational approaches hav...
Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the ... differentially expressed genes. Since most bulk RNA-seq methods assume equal group var...
The Publisher Correction to this article has been published in Genome Biology 2023 24:112
We demonstrate that spatially resolved RNA-seq is ideally suited for high resolution topographical...
Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors ... . We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in bot...
The integration of a viral genome into the host genome has a major impact on the trajectory of the infected cell. Integration location and variation within the associated viral genome can influence both clonal ex...
Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. ... a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs ... data to be applied to non-UMI scRNA-seq datase...
Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. ... database has cataloged software tools for analyzing scRNA-seq data. With the number ...
Here, we use a hybrid allele-specific chromatin binding sequencing (HASCh-seq) approach and identify variations in target binding ... brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s i...
Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requiremen...
Bisulfite treatment of DNA followed by high-throughput sequencing (Bisulfite-seq) is an important method for studying DNA...
We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.
We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to ... but standard methods give biased results on RNA-seq data due to over-detection of dif...
We propose a method for predicting splice graphs that enhances curated gene models using evidence from RNA-Seq and EST alignments. Results obtained using RNA-Seq experiments in Arabidopsis thaliana show that pred...
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from ... fail to efficiently integrate multiple large-scale scRNA-seq dataset...
We present CSDeconv, a computational method that determines locations of transcription factor binding from ChIP-seq data. CSDeconv differs from prior methods in ... accurately. We apply CSDeconv to novel ChIP-seq
Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the...
We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to th...
RNA-Seq and microarray platforms have emerged as important...https://​doi.​org/​expressionplot.​com/​.
Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly...
There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based s...
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean ... analysis pipeline. This opens access for RNA-seq analysts to a large ...
RNA-seq is a powerful technique for identifying and...
The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsit...
The biochemistry of RNA-Seq library preparation results in cDNA fragments that...
Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to ... We validate our method with single-cell ...
Here, we establish patient-derived organoids from colorectal cancer patients and perform single-cell RNA-Seq for pairwise samples from seven patients for...
Nucleotide conversion RNA sequencing techniques interrogate chemical RNA modifications in cellular transcripts, resulting in mismatch-containing reads. Biases in mapping the resulting reads to reference genomes r...
ChIP-Seq is widely used to characterize genome-wide ... chromatin-associated proteins. Although comparison of ChIP-Seq data sets is critical for understanding cell ... method, MAnorm, for quantitative comparison ...
Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcri...
Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. ... (PASs) from 3′ tag-based scRNA-seq. SCAPTURE detects PASs de novo in single ... gene expression, enriching information extracted ...
Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely ... cell barcodes using...
Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived...
Whole genome duplication is a common evolutionary event in plants. Bread wheat (Triticum aestivum L.) is a good model to investigate the impact of paleo- and neoduplications on the organization and function of mo...
We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and ... (individual level differential expression analysis for scRNA-seq). For each gene, ID...
We have developed a new method, SOAPfuse, to identify fusion transcripts from paired-end RNA-Seq data. SOAPfuse applies an improved partial exhaustion ... computing resources. We applied SOAPfuse to RNA-Seq data ...
Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-...
After mapping, RNA-Seq data can be summarized by a sequence...
Identifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does...
There is growing interest in retained introns in a variety of disease contexts including cancer and aging. Many software tools have been developed to detect retained introns from short RNA-seq reads, but reliable...
Here, we present a genome-wide location analysis of FOXA1 and FOXA3 binding sites in HepG2 cells through chromatin immunoprecipitation with detection by sequencing (ChIP-seq) studies and compare these with our pr...
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