:: DESCRIPTION
UniqTag assigns a unique identifier to each gene that is a representative k-mer, a string of length k, selected from the sequence of that gene.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Jackman SD, Bohlmann J, Birol İ.
UniqTag: Content-Derived Unique and Stable Identifiers for Gene Annotation.
PLoS One. 2015 May 28;10(5):e0128026. doi: 10.1371/journal.pone.0128026. PMID: 26020645; PMCID: PMC4447347.
:: DESCRIPTION
FragGeneScan is an application for finding (fragmented) genes in short reads.The software combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Mina Rho, Haixu Tang, and Yuzhen Ye.
FragGeneScan: Predicting Genes in Short and Error-prone Reads.
Nucl. Acids Res., 2010 doi: 10.1093/nar/gkq747
:: DESCRIPTION
REPPER (REPeats and their PERiodicities) is an integrated server that detects and analyzes regions with short gapless repeats in protein sequences or alignments. It finds periodicities by Fourier Transform (FTwin) and internal similarity analysis (REPwin). FTwin assigns numerical values to amino acids that reflect certain properties, for instance hydrophobicity, and gives information on corresponding periodicities. REPwin uses self-alignments and displays repeats that reveal significant internal similarities. Both programs use a sliding window to ensure that different periodic regions within the same protein are detected independently. FTwin and REPwin are complemented by secondary structure prediction (PSIPRED) and coiled coil prediction (COILS), making the server a versatile analysis tool for sequences of fibrous proteins.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation:
M.Gruber, J. Söding , and A.N.Lupas (2005)
REPPER – repeats and their periodicities in fibrous proteins
Nucl. Acids Res., 33(2), W239-43
:: DESCRIPTION
MVQueries is a software of classifying short gene expression time-courses. Short gene expression time-courses monitoring response to toxins are represented as piecewise constant functions, which are modeled as left–right Hidden Markov Models. Our software implements a Bayesian approach to parameter estimation and in inference. Compared to previously published work, we improve prediction accuracy by 7 and 4%, respectively, when classifying toxicology and stress response data and e also reduce running times by at least a factor of 140.
::DEVELOPER
Alexander Schliep’s group for bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2011 Apr 1;27(7):946-52. Epub 2011 Jan 25.
Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions.
Hafemeister C, Costa IG, Sch?nhuth A, Schliep A.