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      Systematically fragmented genes in a multipartite mitochondrial genome

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          Abstract

          Arguably, the most bizarre mitochondrial DNA (mtDNA) is that of the euglenozoan eukaryote Diplonema papillatum. The genome consists of numerous small circular chromosomes none of which appears to encode a complete gene. For instance, the cox1 coding sequence is spread out over nine different chromosomes in non-overlapping pieces (modules), which are transcribed separately and joined to a contiguous mRNA by trans-splicing. Here, we examine how many genes are encoded by Diplonema mtDNA and whether all are fragmented and their transcripts trans-spliced. Module identification is challenging due to the sequence divergence of Diplonema mitochondrial genes. By employing most sensitive protein profile search algorithms and comparing genomic with cDNA sequence, we recognize a total of 11 typical mitochondrial genes. The 10 protein-coding genes are systematically chopped up into three to 12 modules of 60–350 bp length. The corresponding mRNAs are all trans-spliced. Identification of ribosomal RNAs is most difficult. So far, we only detect the 3′-module of the large subunit ribosomal RNA (rRNA); it does not trans-splice with other pieces. The small subunit rRNA gene remains elusive. Our results open new intriguing questions about the biochemistry and evolution of mitochondrial trans-splicing in Diplonema.

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          Most cited references43

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          A new generation of homology search tools based on probabilistic inference.

          Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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            Mitochondrial genomes: anything goes.

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              A dot-matrix program with dynamic threshold control suited for genomic DNA and protein sequence analysis.

              Graphical dot-matrix plots can provide the most complete and detailed comparison of two sequences. Presented here is DOTTER2, a dot-plot program for X-windows which can compare DNA or protein sequences, and also DNA versus protein. The main novel feature of DOTTER is that the user can vary the stringency cutoffs interactively, so that the dot-matrix only needs to be calculated once. This is possible thanks to a 'Greyramp tool' that was developed to change the displayed stringency of the matrix by dynamically changing the greyscale rendering of the dots. The Greyramp tool allows the user to interactively change the lower and upper score limit for the greyscale rendering. This allows exploration of the separation between signal and noise, and fine-grained visualisation of different score levels in the dot-matrix. Other useful features are dot-matrix compression, mouse-controlled zooming, sequence alignment display and saving/loading of dot-matrices. Since the matrix only has to be calculated once and since the algorithm is fast and linear in space, DOTTER is practical to use even for sequences as long as cosmids. DOTTER was integrated in the gene-modelling module of the genomic database system ACEDB3. This was done via the homology viewer BLIXEM in a way that also allows segments from the BLAST suite of searching programs to be superimposed on top of the full dot-matrix. This feature can also be used for very quick finding of the strongest matches. As examples, we analyse a Caenorhabditis elegans cosmid with several tandem repeat families, and illustrate how DOTTER can improve gene modelling.
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                February 2011
                February 2011
                8 October 2010
                8 October 2010
                : 39
                : 3
                : 979-988
                Affiliations
                1Institute of Molecular Genetics, Academy of Sciences of the Czech Republic, Department of Genomics and Bioinformatics, 142 20 Prague, Czech Republic, 2Department of Biochemistry, Université de Montréal, 2900 Edouard-Montpetit, Montreal, Quebec, H3T 1J4 Canada, 3Biology Centre, Institute of Parasitology, Czech Academy of Science and Faculty of Science, University of South Bohemia, 370 05 České Budějovice (Budweis), Czech Republic and 4Robert-Cedergren Centre for Bioinformatics and Genomics, Université de Montréal, Montreal, Quebec, Canada
                Author notes
                *To whom correspondence should be addressed. Tel: +514 343 7936; Fax: +514 343-2210; Email: gertraud.burger@ 123456umontreal.ca

                Present address: William Marande, Museum National d’Histoire Naturelle, Department of RDDM, 61 rue Buffon, 75005 Paris, France.

                Article
                gkq883
                10.1093/nar/gkq883
                3035467
                20935050
                d3206b41-654f-4ab0-935e-493765333281
                © The Author(s) 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 31 May 2010
                : 17 September 2010
                : 19 September 2010
                Categories
                Genomics

                Genetics
                Genetics

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