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 Ogonna Obudulu
Please welcome Ogonna Obudulu to CLiC. He is one of nine PhD students in the BioImprove project (htt...
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About CLiC
The Computational Life science Cluster (CLiC) at KBC aims to stimulate, organize and advance computer based modelling, tools and strategies to understand complex biological systems and e-bioscience at Umeån University. One of our specific goals is to establish a critical and missing link to the ongoing strong experimental research at UmU, e.g. UPSC, UCFB, FuncFiber, MIMS, UCMR and UCMM centers. This in turn, will establish a unique bioinformatics/e-science profile in Umeå
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Activity-dormancy transition in the cambial meristem involves stage specific modulation of auxin response in Hybrid Aspen
K. Baba*, A. Karlberg*, J. Schmidt, J. Schrader, T. R. Hvidsten, L. Bako and R. P. Bhalerao
Proceedings of the National Academy of Sciences
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A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation
N. Street, S. Jansson and T. R. Hvidsten
BMC Plant Biology
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A computer scientist’s guide to the regulatory genome
B. Wilczynski and T. R. Hvidsten
Fundamenta Informaticae
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Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
E. Freyhult, M. Landfors, J. Önskog, T. R. Hvidsten and P. Rydén
BMC Bioinformatics
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OnD-CRF(Web Server)
Order and Disorder prediction using Conditional Random Fields (OnD-CRF) is a new method for accurately predicting the transition between structured and mobile or disordered regions in proteins. OnD-CRF applies CRFs which rely on features generated from the amino acids sequence and from secondary structure prediction. Benchmarking results based on CASP7 targets, and evaluation with respect to several CASP criteria, rank the OnD-CRF model highest among the fully automatic server group.
FISH(Web Server)
Accurate Protein Domain Identification. FISH, which stands for Family Identification with Structure anchored HMMs, is a server for the identification of remote sequence homologues, on the basis of protein domains. The FISH server uses a collection of structure-anchored hidden Markov models, saHMMs, to detect homologous relationships. Establishing the collection of saHMMs requires the following main steps for each domain family: i) selection of representatives for the "midnight ASTRAL set", ii) structural superposition of the representatives, iii) extraction of a multiple sequence alignment from structural superposition, and iv) creation of an associated structure anchored hidden Markov model.
For the definition of homologous structural domains we use the SCOP classification on the family level, and coordinates for the individual domains are from PDB style files in the ASTRAL compendium. In order to avoid bias for very common sequences and to obtain maximum evolutionary spread of the representatives, we use only sequences with a mutual sequence identity below a certain limiting curve, the so-called twilight zone curve.
FragHMMent(Web Server)
FragHMMent is a computational tool for predicting residue-residue contacts from amino acid sequence using multi-data hidden Markov models trained on local neighborhoods (local descriptors of protein structure). It is written in Java.
P. Björkholm, P. Daniluk, A. Kryshtafovych, K. Fidelis, R. Andersson and T. R. Hvidsten. Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts. Bioinformatics 25: 1264-1270, 2009.
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