The main stages of the pipeline are indicated in the boxes.

The text under each box indicates the main software or tool that is utilized in that stage. Python scripts tie the stages together, provide a user interface, allow for configuration of options, and output results in a variety of formats. The web interface of the software runs the pipeline from a server, allowing the pipeline to be run with no software installation required for the user.</p

Supplementary data.

Prokaryotic chromosomes contain numerous small open reading frames (ORFs) of less than 200 bases. Since high-throughput proteomics methods often miss proteins containing fewer than 60 amino acids, it is difficult to decern if they encode proteins. Recent studies have revealed that many small proteins are membrane proteins with a single membrane-anchoring α-helix. As membrane anchoring or transmembrane motifs are accurately identifiable with high confidence using computational algorithms like Phobius and TMHMM, small membrane proteins (SMPS) can be predicted with high accuracy. This study employed a systematic approach, utilizing well-verified algorithms such as Orfipy, Phobius, and Blast to identify SMPs in prokaryotic organisms. Our main search parameters targeted candidate SMPs with an open reading frame between 60–180 nucleotides, a membrane-anchoring or transmembrane region 15 and 30 amino acids long, and sequence conservation among other microorganisms. Our findings indicate that each prokaryote possesses many SMPs, with some identified in the intergenic regions of currently annotated chromosomes. More extensively studied microorganisms, such as Escherichia coli and Bacillus subtilis, have more SMPs identified in their genomes compared to less studied microorganisms, suggesting the possibility of undiscovered SMPs in less studied microorganisms. In this study, we describe the common SMPs identified across various microorganisms and explore their biological roles. We have also developed a software pipeline and an accompanying online interface for discovering SMPs (http://cs.indstate.edu/pro-smp-finder). This resource aims to assist researchers in identifying new SMPs encoded in microbial genomes of interest.</div

Additional file 1: of Identification of genome-wide non-canonical spliced regions and analysis of biological functions for spliced sequences using Read-Split-Fly

Comparison of running time and memory usage between RSR and RSF for 70 ENCODE samples. This file contains detailed memory and running time comparison for 70 ENCODE samples. (XLSX 18 kb

Additional file 3: of Read-Split-Run: an improved bioinformatics pipeline for identification of genome-wide non-canonical spliced regions using RNA-Seq data

Statistics reported by RSR for 100 test cases for the Tg sample. This file contains all supplementary results for 100 test cases for the Tg sample. (XLSX 92 kb

Epimedium sempervirens Nakai

原著和名: トキハイカリサウ科名: メギ科 = Berberidaceae採集地: 島根県 隠岐 島後 (隠岐 島後)採集日: 1976/5/9採集者: 萩庭丈壽整理番号: JH002351国立科学博物館整理番号: TNS-VS-95235

Additional file 5: of Read-Split-Run: an improved bioinformatics pipeline for identification of genome-wide non-canonical spliced regions using RNA-Seq data

Total number of spliced regions identified by RSR in the human ENCODE RNA-Seq dataset. This file includes all supplementary results for number of supporting reads, splice length, range of supporting reads (spliced regions) identified by RSR in the human ENCODE RNA-Seq dataset. (XLSX 1411 kb