The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible...
Read More »Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data
Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most...
Read More »IBRAP – integrated benchmarking single-cell RNA-sequencing analytical pipeline
Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) is a powerful tool to study cellular heterogeneity. The high dimensional data...
Read More »DeepMAPS – biological network inference from scMulti-omics
Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing...
Read More »A guide to gene-centric analysis using TreeSAPP
Gene-centric analysis is commonly used to chart the structure, function, and activity of microbial communities in natural and engineered environments. A common approach is to create...
Read More »DeepBIO – automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis.
Shandong University researchers have developed DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables...
Read More »RNAlysis – analyze your RNA sequencing data without writing a single line of code
Amongst the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further...
Read More »vAMPirus – a versatile amplicon processing and analysis program for studying viruses
Amplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Researchers at Rice University have...
Read More »Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data
Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore...
Read More »L-RAPiT: Long Read Analysis Pipeline for Transcriptomics
L-RAPiT: Long Read Analysis Pipeline for Transcriptomics is an easy-to-use publicly available pipeline which allows for analysis of long read RNA-sequencing data within a cloud environment; namely, Google Colaboratory. L-RAPiT is available at the following address: https://github.com/Theo-Nelson/long-read-sequencing-pipeline Citation: Nelson, T.M.; ...
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