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Search Results: 1 - 10 of 11224 matches for " Mario Stanke "
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AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome
Mario Stanke, Ana Tzvetkova, Burkhard Morgenstern
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-s1-s11
Abstract: 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.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.With an increasing number of completely or partially sequenced genomes, computational prediction of protein-coding genes has become one of the most active fields of research in bioinformatics. This task is particularly challenging for eukaryotes, where protein-coding exons are usually separated by non-coding introns of varying length. Previous studies have shown that the accuracy of the currently available tools for gene finding in human is not satisfactory [1].AUGUSTUS is a method for gene finding in eukaryotes [2]. The original version of the program used intrinsic information only, that is, information contained in the genomic sequence that is to be annotated. A recent extension of the program is also able to integrate extrinsic information from arbitrary sources for improved prediction accuracy [3].At the ENCODE genome annotation assessment project (EGASP) workshop that took place in May 20
Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources
Mario Stanke, Oliver Sch?ffmann, Burkhard Morgenstern, Stephan Waack
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-62
Abstract: 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.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.Finding protein-coding genes in eukaryotic genomic sequences with in-silico methods remains an important challenge in computational genomics, despite many years of intensive research work. Existing methods fall into two groups with respect to the data they utilize. The first group consists of ab initio programs which use only the query genomic sequence as input. Examples are the programs GENSCAN [1], AUGUSTUS [2] and HMMGene [3] which are HMM-based and GENEID [4]. The second group of gene-finding methods, extrinsic methods, comprises all programs which use data other than the query genomic sequence. Some extrinsic methods use genomic sequences from other species. A cross-species comparison of genomic sequences
Scipio: Using protein sequences to determine the precise exon/intron structures of genes and their orthologs in closely related species
Oliver Keller, Florian Odronitz, Mario Stanke, Martin Kollmar, Stephan Waack
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-278
Abstract: Scipio is a tool based on the alignment program BLAT to determine the precise gene structure given a protein sequence and a genome sequence. It identifies intron-exon borders and splice sites and is able to cope with sequencing errors and genes spanning several contigs in genomes that have not yet been assembled to supercontigs or chromosomes. Instead of producing a set of hits with varying confidence, Scipio gives the user a coherent summary of locations on the genome that code for the query protein. The output contains information about discrepancies that may result from sequencing errors. Scipio has also successfully been used to find homologous genes in closely related species. Scipio was tested with 979 protein queries against 16 arthropod genomes (intra species search). For cross-species annotation, Scipio was used to annotate 40 genes from Homo sapiens in the primates Pongo pygmaeus abelii and Callithrix jacchus. The prediction quality of Scipio was tested in a comparative study against that of BLAT and the well established program Exonerate.Scipio is able to precisely map a protein query onto a genome. Even in cases when there are many sequencing errors, or when incomplete genome assemblies lead to hits that stretch across multiple target sequences, it very often provides the user with the correct determination of intron-exon borders and splice sites, showing an improved prediction accuracy compared to BLAT and Exonerate. Apart from being able to find genes in the genome that encode the query protein, Scipio can also be used to annotate genes in closely related species.In the post-genome era, sequence data is the entry point for many studies. Often, it is essential to obtain the correct genomic DNA sequences of eukaryotic genes because of the information contained in non-coding regions. For example, the intron regions contain important sites for the regulation of gene transcription, like enhancers, repressors, and silencers [1]. Transcription initiator seque
ClassyFlu: Classification of Influenza A Viruses with Discriminatively Trained Profile-HMMs
Sandra Van der Auwera, Ingo Bulla, Mario Ziller, Anne Pohlmann, Timm Harder, Mario Stanke
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0084558
Abstract: Accurate and rapid characterization of influenza A virus (IAV) hemagglutinin (HA) and neuraminidase (NA) sequences with respect to subtype and clade is at the basis of extended diagnostic services and implicit to molecular epidemiologic studies. ClassyFlu is a new tool and web service for the classification of IAV sequences of the HA and NA gene into subtypes and phylogenetic clades using discriminatively trained profile hidden Markov models (HMMs), one for each subtype or clade. ClassyFlu merely requires as input unaligned, full-length or partial HA or NA DNA sequences. It enables rapid and highly accurate assignment of HA sequences to subtypes H1–H17 but particularly focusses on the finer grained assignment of sequences of highly pathogenic avian influenza viruses of subtype H5N1 according to the cladistics proposed by the H5N1 Evolution Working Group. NA sequences are classified into subtypes N1–N10. ClassyFlu was compared to semiautomatic classification approaches using BLAST and phylogenetics and additionally for H5 sequences to the new “Highly Pathogenic H5N1 Clade Classification Tool” (IRD-CT) proposed by the Influenza Research Database. Our results show that both web tools (ClassyFlu and IRD-CT), although based on different methods, are nearly equivalent in performance and both are more accurate and faster than semiautomatic classification. A retraining of ClassyFlu to altered cladistics as well as an extension of ClassyFlu to other IAV genome segments or fragments thereof is undemanding. This is exemplified by unambiguous assignment to a distinct cluster within subtype H7 of sequences of H7N9 viruses which emerged in China early in 2013 and caused more than 130 human infections. http://bioinf.uni-greifswald.de/ClassyFl?u is a free web service. For local execution, the ClassyFlu source code in PERL is freely available.
A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes
Anne-Kathrin Schultz, Ming Zhang, Thomas Leitner, Carla Kuiken, Bette Korber, Burkhard Morgenstern, Mario Stanke
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-265
Abstract: 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.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.Profile Hidden Markov Models [1] are a popular way of modelling nucleic-acid or protein sequence families for database searching, see [2] for a review. Like other Hidden Markov Models (HMMs), profile HMMs consist of so-called states that can emit symbols of the underlying alphabet, i.e. nucleotides or amino acids [3]. Transitions are possible between these states, and a DNA or protein sequence is thought to be generated by a path Q through the model beginning with a special begin state and ending with an end state. There are probabilities (a) for possible transitions from one state to another and (b) for the emission of symbols at a given state. The states together with the possible transitions between them are called the topology of the model while the corresponding transition and emission probabilities are called its parameters. A sequence S is generated by the model with a certain probability P(S). In general, a sequence S can be generated by more than one path Q through the mo
Detection of viral sequence fragments of HIV-1 subfamilies yet unknown
Thomas Unterthiner, Anne-Kathrin Schultz, Jan Bulla, Burkhard Morgenstern, Mario Stanke, Ingo Bulla
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-93
Abstract: We have developed Unknown Subtype Finder (USF), an algorithm based on a probabilistic model, which automatically determines which parts of an input sequence originate from a subtype yet unknown. The underlying model is based on a simple profile hidden Markov model (pHMM) for each known subtype and an additional pHMM for an unknown subtype. The emission probabilities of the latter are estimated using the emission frequencies of the known subtypes by means of a (position-wise) probabilistic model for the emergence of new subtypes. We have applied USF to SIV and HIV-1 sequences formerly classified as having emerged from an unknown subtype. Moreover, we have evaluated its performance on artificial HIV-1 recombinants and non-recombinant HIV-1 sequences. The results have been compared with the corresponding results of the BI.Our results demonstrate that USF is suitable for detecting segments in HIV-1 sequences stemming from yet unknown subtypes. Comparing USF with the BI shows that our algorithm performs as good as the BI or better.An accurate and reliable classification of viral sequences data for human immunodeficiency virus-1 (HIV-1) and some other viruses of interest is important for epidemiological studies. It facilitates the understanding of the influence of genetic diversity on host immune response and provides therapeutic decision support [1-3]. As HIV-1 is, however, one of the genetically most variable viruses and genomic recombinations are frequent in HIV-1 [4], the task of classifying corresponding viral sequence data is a challenging one.HIV-1 is classified into three main phylogenetic groups (M, N, and O), introduced into humans by separate zoonotic events (all stemming from simian immunodeficiency viruses (SIVs) in chimpanzees [5]. The M group is responsible for the HIV pandemic, and it is divided into nine subtypes, with subtype A and F being subdivided into subsubtypes [6]. Inter-subtype recombination occurs very frequently among HIV-1 subtypes [7]: So far
The role of recombination in the emergence of a complex and dynamic HIV epidemic
Ming Zhang, Brian Foley, Anne-Kathrin Schultz, Jennifer P Macke, Ingo Bulla, Mario Stanke, Burkhard Morgenstern, Bette Korber, Thomas Leitner
Retrovirology , 2010, DOI: 10.1186/1742-4690-7-25
Abstract: The circulating recombinant form CRF02_AG, common in West Central Africa, appears to result from recombination events that occurred early in the divergence between subtypes A and G, followed by additional recent recombination events that contribute to the breakpoint pattern defining the current recombinant lineage. This finding also corrects a recent claim that G is a recombinant and a descendant of CRF02, which was suggested to be a pure subtype. The BC and BF recombinants in China and South America, respectively, are derived from recent recombination between contemporary parental lineages. Shared breakpoints in South America BF recombinants indicate that the HIV-1 epidemics in Argentina and Brazil are not independent. Therefore, the contemporary HIV-1 epidemic has recombinant lineages of both ancient and more recent origins.Taken together, we show that these recombinant lineages, which are highly prevalent in the current HIV epidemic, are a mixture of ancient and recent recombination. The HIV pandemic is moving towards having increasing complexity and higher prevalence of recombinant forms, sometimes existing as "families" of related forms. We find that the classification of some CRF designations need to be revised as a consequence of (1) an estimated > 5% error in the original subtype assignments deposited in the Los Alamos sequence database; (2) an increasing number of CRFs are defined while they do not readily fit into groupings for molecular epidemiology and vaccine design; and (3) a dynamic HIV epidemic context.Retroviral recombination introduces rapid, large genetic alternations [1-3], and can repair genome damage [4,5]. Recombination is a major force in HIV evolution, occurring at an estimated rate of at least 2.8 crossovers per genome per cycle [6]. Recently the effective recombination rate, i.e., the product of super-infection and crossovers, was estimated to be on a similar frequency as the nucleotide substitution rate within patients (1.4 × 10-5 recombi
O papel do professor no ensino de alem?o para o fim específico da leitura
Stanke, Roberta Cristina Sol Fernandes;
Revista Brasileira de Linguística Aplicada , 2011, DOI: 10.1590/S1984-63982011000400008
Abstract: this paper aims to analyze the teacher's role in a german course which has the specific purpose to develop the reading skill in this foreign language. the research was based on field notes in a german course for reading, including diaries, interviews, questionnaires, documental analysis and audio and video recordings. the results showed the multifaceted role of the foreign language teacher for specific purposes, who teaches the foreign language, analyses the needs of the target situation and of the learning situation, selects and produces didactic material, makes the learner aware of what the reading process is and foment the development of reading strategies.
HH 46/47: Also a parsec scale flow
Thomas Stanke,Mark J. McCaughrean,Hans Zinnecker
Physics , 1999,
Abstract: We report the discovery of a pair of large Herbig-Haro type structures roughly 10 arcminutes (1.3 pc) north-east and south-west of the source driving the well-known HH 46/47 Herbig-Haro jet in new deep emission-line images made using the Wide Field Imager on the ESO/MPG La Silla 2.2-m telescope. These new images suggest that the HH 46/47 outflow is much more extensive than previously assumed, extending over a total of 2.6 pc on the sky, or over 3 pc in space, when deprojected. HH 46/47 thus also belongs to the recently-discovered class of giant Herbig-Haro flows.
Giant protostellar outflows revealed by infrared imaging
Thomas Stanke,Mark J. McCaughrean,Hans Zinnecker
Physics , 2000,
Abstract: We present new infrared data from a survey for embedded protostellar jets in the Orion A cloud. This survey makes use of the S(1) v = 1-0 line of molecular hydrogen at lambda = 2.12 mum to search for infrared jets deep inside the cloud and thus hidden from view at optical wavelengths. We present data on the three flows associated with the Herbig-Haro objects HH 43/38 & HH 64, HH 65, and the L1641-N giant flow. HH 43, HH 38 and HH 64 are part of one H_2 flow extending over at least 1.4 pc. We identify a deeply embedded 1.3 mm and IRAS source (HH 43 MMS1 = IRAS 05355-0709C) as the likely driving source, while the infrared source previously assumed to drive HH 43/38 (HH 43-IRS1 = IRAS 05357-0710) is seen to drive a smaller jet. The morphology of HH 43-IRS1 suggests that it is a star+disk system seen close to edge-on. We identify another large H_2 flow apparently comprising the L1641-S3 CO outflow and the redshifted lobe of the L1641-S CO outflow containing HH 65. This flow extends over at least 2.6 pc and appears strongly curved. It is driven by L1641-S3 IRS, a deeply embedded 1.3 mm and IRAS source (L1641-S3 MMS1 = IRAS 05375-0731). Finally, we have found some additional large H_2 features to the east of V 380 Ori and the HH 1/2 system, which probably outline another part of the L1641-N outflow. The molecular flow MB 20/21, which extends to the south from V 380 Ori, also appears to be a part of the L1641-N outflow.
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