Biomedical subjects

Mario Stanke

Publications and source records attributed to Mario Stanke.

8 recordsLinked to original sources

PubMed2006

AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome.

BACKGROUND: A large number of gene prediction programs for the human genome exist. These annotation tools use a variety of methods and data sources. In the recent ENCODE genome annotation assessment project (EGASP), some of the most commonly used and recently developed gene-prediction programs were systematically evaluated and compared on test data from the human genome. AUGUSTUS was among the tools that were tested in this project. RESULTS: AUGUSTUS can be used as an ab initio program, that is, as a program that uses only one single genomic sequence as input information. In addition, it is able to combine information from the genomic sequence under study with external hints from various sources of information. For EGASP, we used genomic sequence alignments as well as alignments to expressed sequence tags (ESTs) and protein sequences as additional sources of information. Within the category of ab initio programs AUGUSTUS predicted significantly more genes correctly than any other ab initio program. At the same time it predicted the smallest number of false positive genes and the smallest number of false positive exons among all ab initio programs. The accuracy of AUGUSTUS could be further improved when additional extrinsic data, such as alignments to EST, protein and/or genomic sequences, was taken into account. CONCLUSION: AUGUSTUS turned out to be the most accurate ab initio gene finder among the tested tools. Moreover it is very flexible because it can take information from several sources simultaneously into consideration.

Amino Acid Sequence↗

PubMed2006

AUGUSTUS: ab initio prediction of alternative transcripts.

AUGUSTUS is a software tool for gene prediction in eukaryotes based on a Generalized Hidden Markov Model, a probabilistic model of a sequence and its gene structure. Like most existing gene finders, the first version of AUGUSTUS returned one transcript per predicted gene and ignored the phenomenon of alternative splicing. Herein, we present a WWW server for an extended version of AUGUSTUS that is able to predict multiple splice variants. To our knowledge, this is the first ab initio gene finder that can predict multiple transcripts. In addition, we offer a motif searching facility, where user-defined regular expressions can be searched against putative proteins encoded by the predicted genes. The AUGUSTUS web interface and the downloadable open-source stand-alone program are freely available from http://augustus.gobics.de.

Alternative Splicing↗

PubMed2006

jpHMM at GOBICS: a web server to detect genomic recombinations in HIV-1.

Detecting recombinations in the genome sequence of human immunodeficiency virus (HIV-1) is crucial for epidemiological studies and for vaccine development. Herein, we present a web server for subtyping and localization of phylogenetic breakpoints in HIV-1. Our software is based on a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach proposed by Spang et al. The input data for our server is a partial or complete genome sequence from HIV-1; our tool assigns regions of the input sequence to known subtypes of HIV-1 and predicts phylogenetic breakpoints. jpHMM is available online at http://jphmm.gobics.de/.

Genome, Viral↗

PubMed2006

A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes.

BACKGROUND: Jumping alignments have recently been proposed as a strategy to search a given multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events. RESULTS: We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied our method to a large set of genome sequences from human immunodeficiency virus (HIV) and hepatitis C virus (HCV) as well as to simulated recombined genome sequences. CONCLUSION: Our results demonstrate that jumps in our jumping profile HMM often correspond to recombination breakpoints; our approach can therefore be used to detect recombinations in genomic sequences. The recombination breakpoints identified by jpHMM were found to be significantly more accurate than breakpoints defined by traditional methods based on comparing single representative sequences.

Algorithms↗

PubMed2006

Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources.

BACKGROUND: In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. RESULTS: We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. CONCLUSION: Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.

Algorithms↗

PubMed2005

AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints.

We present a WWW server for AUGUSTUS, a software for gene prediction in eukaryotic genomic sequences that is based on a generalized hidden Markov model, a probabilistic model of a sequence and its gene structure. The web server allows the user to impose constraints on the predicted gene structure. A constraint can specify the position of a splice site, a translation initiation site or a stop codon. Furthermore, it is possible to specify the position of known exons and intervals that are known to be exonic or intronic sequence. The number of constraints is arbitrary and constraints can be combined in order to pin down larger parts of the predicted gene structure. The result then is the most likely gene structure that complies with all given user constraints, if such a gene structure exists. The specification of constraints is useful when part of the gene structure is known, e.g. by expressed sequence tag or protein sequence alignments, or if the user wants to change the default prediction. The web interface and the downloadable stand-alone program are available free of charge at http://augustus.gobics.de/submission.

Animals↗

PubMed2004

AUGUSTUS: a web server for gene finding in eukaryotes.

We present a www server for AUGUSTUS, a novel software program for ab initio gene prediction in eukaryotic genomic sequences. Our method is based on a generalized Hidden Markov Model with a new method for modeling the intron length distribution. This method allows approximation of the true intron length distribution more accurately than do existing programs. For genomic sequence data from human and Drosophila melanogaster, the accuracy of AUGUSTUS is superior to existing gene-finding approaches. The advantage of our program becomes apparent especially for larger input sequences containing more than one gene. The server is available at http://augustus.gobics.de.

Animals↗

PubMed2003

Gene prediction with a hidden Markov model and a new intron submodel.

MOTIVATION: The problem of finding the genes in eukaryotic DNA sequences by computational methods is still not satisfactorily solved. Gene finding programs have achieved relatively high accuracy on short genomic sequences but do not perform well on longer sequences with an unknown number of genes in them. Here existing programs tend to predict many false exons. RESULTS: We have developed a new program, AUGUSTUS, for the ab initio prediction of protein coding genes in eukaryotic genomes. The program is based on a Hidden Markov Model and integrates a number of known methods and submodels. It employs a new way of modeling intron lengths. We use a new donor splice site model, a new model for a short region directly upstream of the donor splice site model that takes the reading frame into account and apply a method that allows better GC-content dependent parameter estimation. AUGUSTUS predicts on longer sequences far more human and drosophila genes accurately than the ab initio gene prediction programs we compared it with, while at the same time being more specific. AVAILABILITY: A web interface for AUGUSTUS and the executable program are located at http://augustus.gobics.de.

Algorithms↗