TY  - JOUR
AB  - Background: Comparative analyses of chromosomal gene orders are successfully used to predict gene clusters in
bacterial and fungal genomes. Present models for detecting sets of co-localized genes in chromosomal sequences
require prior knowledge of gene family assignments of genes in the dataset of interest. These families are often
computationally predicted on the basis of sequence similarity or higher order features of gene products. Errors
introduced in this process amplify in subsequent gene order analyses and thus may deteriorate gene cluster
prediction.
Results: In this work, we present a new dynamic model and efficient computational approaches for gene cluster
prediction suitable in scenarios ranging from traditional gene family-based gene cluster prediction, via multiple
conflicting gene family annotations, to gene family-free analysis, in which gene clusters are predicted solely on the
basis of a pairwise similarity measure of the genes of different genomes. We evaluate our gene family-free model
against a gene family-based model on a dataset of 93 bacterial genomes.
Conclusions: Our model is able to detect gene clusters that would be also detected with well-established gene
family-based approaches. Moreover, we show that it is able to detect conserved regions which are missed by gene
family-based methods due to wrong or deficient gene family assignments.
DA  - 2014
DO  - 10.1186/1471-2164-15-S6-S2
LA  - eng
IS  - Suppl. 6: Proc. of RECOMB-CG 2014
PY  - 2014
SN  - 1471-2164
T2  - BMC Genomics
TI  - Identifying Gene Clusters by Discovering Common Intervals in Indeterminate Strings
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-26870336
Y2  - 2024-11-24T18:53:32
ER  -