MCScan is an algorithm able to scan multiple genomes or subgenomes in order to identify putative homologous chromosomal regions, and align these regions using genes as anchors. The MCScanX toolkit implements an adjusted MCScan algorithm for detection of synteny and collinearity that extends the original software by incorporating 14 utility programs for visualization of results and additional downstream analyses. Applications of MCScanX to several sequenced plant genomes and gene families are shown as examples. MCScanX can be used to effectively analyze chromosome structural changes, and reveal the history of gene family expansions that might contribute to the adaptation of lineages and taxa. An integrated view of various modes of gene duplication can supplement the traditional gene tree analysis in specific families. The source code and documentation of MCScanX are freely available at http://chibba.pgml.uga.edu/mcscan2/.

MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity

Expression.- Spectral Clustering Gene Ontology Terms to Group Genes by Function.- Dynamic De-Novo Prediction of microRNAs Associated with Cell Conditions: A Search Pruned by Expression.- Clustering Gene Expression Series with Prior Knowledge.- A Linear Time Biclustering Algorithm for Time Series Gene Expression Data.- Time-Window Analysis of Developmental Gene Expression Data with Multiple Genetic Backgrounds.- Phylogeny.- A Lookahead Branch-and-Bound Algorithm for the Maximum Quartet Consistency Problem.- Computing the Quartet Distance Between Trees of Arbitrary Degree.- Using Semi-definite Programming to Enhance Supertree Resolvability.- An Efficient Reduction from Constrained to Unconstrained Maximum Agreement Subtree.- Pattern Identification in Biogeography.- On the Complexity of Several Haplotyping Problems.- A Hidden Markov Technique for Haplotype Reconstruction.- Algorithms for Imperfect Phylogeny Haplotyping (IPPH) with a Single Homoplasy or Recombination Event.- Networks.- A Faster Algorithm for Detecting Network Motifs.- Reaction Motifs in Metabolic Networks.- Reconstructing Metabolic Networks Using Interval Analysis.- Genome Rearrangements.- A 1.375-Approximation Algorithm for Sorting by Transpositions.- A New Tight Upper Bound on the Transposition Distance.- Perfect Sorting by Reversals Is Not Always Difficult.- Minimum Recombination Histories by Branch and Bound.- Sequences.- A Unifying Framework for Seed Sensitivity and Its Application to Subset Seeds.- Generalized Planted (l,d)-Motif Problem with Negative Set.- Alignment of Tandem Repeats with Excision, Duplication, Substitution and Indels (EDSI).- The Peres-Shields Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity.- Multiple Structural RNA Alignment with Lagrangian Relaxation.- Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons.- Ortholog Clustering on a Multipartite Graph.- Linear Time Algorithm for Parsing RNA Secondary Structure.- A Compressed Format for Collections of Phylogenetic Trees and Improved Consensus Performance.- Structure.- Optimal Protein Threading by Cost-Splitting.- Efficient Parameterized Algorithm for Biopolymer Structure-Sequence Alignment.- Rotamer-Pair Energy Calculations Using a Trie Data Structure.- Improved Maintenance of Molecular Surfaces Using Dynamic Graph Connectivity.- The Main Structural Regularities of the Sandwich Proteins.- Discovery of Protein Substructures in EM Maps.

Algorithms in Bioinformatics: 5th International Workshop, WABI 2005, Mallorca, Spain, October 3-6, 2005, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Bioinformatics)

Chemoreception is a biological process essential for the survival of animals, as it allows the recognition of important volatile cues for the detection of food, egg-laying substrates, mates, or predators, among other purposes. Furthermore, its role in pheromone detection may contribute to evolutionary processes, such as reproductive isolation and speciation. This key role in several vital biological processes makes chemoreception a particularly interesting system for studying the role of natural selection in molecular adaptation. Two major gene families are involved in the perireceptor events of the chemosensory system: the odorantbinding protein (OBP) and chemosensory protein (CSP) families. Here, we have conducted an exhaustive comparative genomic analysis of these gene families in 20 Arthropoda species. We show that the evolution of the OBP and CSP gene families is highly dynamic, with a high number of gains and losses of genes, pseudogenes, and independent origins of subfamilies. Taken together, our data clearly support the birth-and-death model for the evolution of these gene families with an overall high gene turnover rate. Moreover, we show that the genome organization of the two families is significantly more clustered than expected by chance and, more important, that this pattern appears to be actively maintained across the Drosophila phylogeny. Finally, we suggest the homologous nature of the OBP and CSP gene families, dating back their most recent common ancestor after the terrestrialization of Arthropoda (380‐450 Ma) and we propose a scenario for the origin and diversification of these families.

/pdf/comparative-genomics-of-the-odorant-binding-and-chemosensory-4ch9tosqk1.pdf

Comparative Genomics of the Odorant-Binding and Chemosensory Protein Gene Families across the Arthropoda: Origin and Evolutionary History of the Chemosensory System

Mycobacterium abscessus is an emerging rapidly growing mycobacterium (RGM) causing a pseudotuberculous lung disease to which patients with cystic fibrosis (CF) are particularly susceptible. We report here its complete genome sequence. The genome of M. abscessus (CIP 104536T) consists of a 5,067,172-bp circular chromosome including 4920 predicted coding sequences (CDS), an 81-kb full-length prophage and 5 IS elements, and a 23-kb mercury resistance plasmid almost identical to pMM23 from Mycobacterium marinum. The chromosome encodes many virulence proteins and virulence protein families absent or present in only small numbers in the model RGM species Mycobacterium smegmatis. Many of these proteins are encoded by genes belonging to a “mycobacterial” gene pool (e.g. PE and PPE proteins, MCE and YrbE proteins, lipoprotein LpqH precursors). However, many others (e.g. phospholipase C, MgtC, MsrA, ABC Fe(3+) transporter) appear to have been horizontally acquired from distantly related environmental bacteria with a high G+C content, mostly actinobacteria (e.g. Rhodococcus sp., Streptomyces sp.) and pseudomonads. We also identified several metabolic regions acquired from actinobacteria and pseudomonads (relating to phenazine biosynthesis, homogentisate catabolism, phenylacetic acid degradation, DNA degradation) not present in the M. smegmatis genome. Many of the “non mycobacterial” factors detected in M. abscessus are also present in two of the pathogens most frequently isolated from CF patients, Pseudomonas aeruginosa and Burkholderia cepacia. This study elucidates the genetic basis of the unique pathogenicity of M. abscessus among RGM, and raises the question of similar mechanisms of pathogenicity shared by unrelated organisms in CF patients.

/pdf/non-mycobacterial-virulence-genes-in-the-genome-of-the-149u9tr9sd.pdf

Non Mycobacterial Virulence Genes in the Genome of the Emerging Pathogen Mycobacterium abscessus

https://www.cell.com/trends/biochemical-sciences/pdf/S0968-0004(08)00148-5.pdf

Arrangements in the modular evolution of proteins

This book presents a practical approach to string matching problems, focusing on the algorithms and implementations that perform best in practice. It covers searching for simple, multiple, and extended strings, as well as regular expressions, exactly and approximately. It includes all of the most significant new developments in complex pattern searching. The clear explanations, step-by-step examples, algorithms pseudo-code, and implementation efficiency maps will enable researchers, professionals, and students in bioinformatics, computer science, and software engineering to choose the most appropriate algorithms for their applications.

Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences

Comparative genomics is a growing field in computational biology, and one of its typical problem is the identification of sets of orthologous genes that have virtually the same function in several genomes. Many different bioinformatics approaches have been proposed to define these groups, often based on the detection of sets of genes that are "not too far" in all genomes. In this paper, we propose a unifying concept, called gene teams, which can be adapted to various notions of distance. We present two algorithms for identifying gene teams formed by n genes placed on m linear chromosomes. The first one runs in O(m2n2) time, and follows a direct and simple approach. The second one is more tricky, but its running time is O(mnlog2(n)). Both algorithms require linear space. We also discuss extensions to circular chromosomes that achieve the same complexity.

The Algorithmic of Gene Teams

Software note: Gene teams: a new formalization of gene clusters for comparative genomics

The detection, across several genomes, of local conservation of gene content and proximity considerably helps the prediction of features of interest, such as gene fusions or physical and functional interactions. Here, we want to process realistic models of chromosomes, in which genes (or genomic segments of several genes) can be duplicated within a chromosome, or be absent from some other chromosome(s). Our approach adopts the technique of temporarily forgetting genes and working directly with protein "domains" such as those found in Pfam. This allows the detection of strings of domains that are conserved in their content, but not necessarily in their order, which we refer to as domain teams. The prominent feature of the method is that it relaxes the rigidity of the orthology criterion and avoids many of the pitfalls of gene-families identification methods, often hampered by multidomain proteins or low levels of sequence similarity. This approach, that allows both inter- and intrachromosomal comparisons, proves to be more sensitive than the classical methods based on pairwise sequence comparisons, particularly in the simultaneous treatment of many species. The automated and fast detection of domain teams, together with its increased sensitivity at identifying segments of identical (protein-coding) gene contents as well as gene fusions, should prove a useful complement to other existing methods.

/pdf/identification-of-genomic-features-using-microsyntenies-of-3jkbzaagdi.pdf

Identification of genomic features using microsyntenies of domains: domain teams.

http://www-igm.univ-mlv.fr/~beal/Recherche/Publications/Fusion-geneteam.pdf

Mathieu Raffinot

Papers

Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences

The Algorithmic of Gene Teams

Software note: Gene teams: a new formalization of gene clusters for comparative genomics

Identification of genomic features using microsyntenies of domains: domain teams.

An algorithmic view of gene teams