RNA-Seq is becoming a promising replacement to microarrays in transcriptome profiling and differential gene expression study. Technical improvements have decreased sequencing costs and, as a result, the size and number of RNA-Seq datasets have increased rapidly. However, the increasing volume of data from large-scale RNA-Seq studies poses a practical challenge for data analysis in a local environment. To meet this challenge, we developed Stormbow, a cloud-based software package, to process large volumes of RNA-Seq data in parallel. The performance of Stormbow has been tested by practically applying it to analyse 178 RNA-Seq samples in the cloud. In our test, it took 6 to 8 hours to process an RNA-Seq sample with 100 million reads, and the average cost was $3.50 per sample. Utilizing Amazon Web Services as the infrastructure for Stormbow allows us to easily scale up to handle large datasets with on-demand computational resources. Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. Stormbow can be freely downloaded and can be used out of box to process Illumina RNA-Seq datasets.
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http://dx.doi.org/10.1155/2013/481545 | DOI Listing |
Bioinform Adv
June 2024
Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, United States.
Motivation: Bispecific antibodies (bsAbs) that bind to two distinct surface antigens on cancer cells are emerging as an appealing therapeutic strategy in cancer immunotherapy. However, considering the vast number of surface proteins, experimental identification of potential antigen pairs that are selectively expressed in cancer cells and not in normal cells is both costly and time-consuming. Recent studies have utilized large bulk RNA-seq databases to propose bispecific targets for various cancers.
View Article and Find Full Text PDFPNAS Nexus
January 2025
Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Here, we present Link-Seq, a highly efficient droplet microfluidic method for combined sequencing of antibody-encoding genes and the transcriptome of individual B cells at large scale. The method is based on 3' barcoding of the transcriptome and subsequent single-molecule PCR in droplets, which freely shift the barcode along specific gene regions, such as the antibody heavy- and light-chain genes. Using the immune repertoire of COVID-19 patients and healthy donors as a model system, we obtain up to 91.
View Article and Find Full Text PDFBrief Bioinform
November 2024
State Key Laboratory of Cellular Stress Biology, Xiang'an Hospital, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, No. 4221, Xiang'an South Road, Xiamen, Fujian 361102, China.
Acute myeloid leukemia (AML) demonstrates significant cellular heterogeneity in both leukemic and immune cells, providing valuable insights into clinical outcomes. Here, we constructed an AML single-cell transcriptome atlas and proposed sciNMF workflow to systematically dissect underlying cellular heterogeneity. Notably, sciNMF identified 26 leukemic and immune cell states that linked to clinical variables, mutations, and prognosis.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
School of Mathematics/Harbin Institute of Technology, Harbin, China.
The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets.
View Article and Find Full Text PDFNucleic Acids Res
January 2025
Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania.
The expansion of single-cell analytical techniques has empowered the exploration of diverse biological questions at the individual cells. Droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly widely used due to their high-throughput capabilities and small reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, their relatively high cost limits the ability to profile large numbers of cells and samples.
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