Approach For Functional Annotation Research Articles

A combined metagenome and disease enrichment analysis of nitrosamines present in smokeless tobacco, wiz using molecular docking and functional annotation approach

Smokeless tobacco products (SLTs) are a major concern due to the associated health risks of the carcinogens, toxins, dynamic microbial system and other harmful chemicals present in them. Procured tobacco leaves are sweetened, flavored and processed to yield the various noncombustible forms of SLTs. These SLTs contain a discrete microbial ecosystem which may contribute to the formation of tobacco specific nitrosamines (TSNAs), endotoxins and other pro inflammatory components. In the present study Whole Metagenome Sequencing was carried out for the exploration of microbial communities followed by their functional profiling. In addition to this, novel targets for TSNAs were predicted using the Swiss Target Prediction tool. Further, disease enrichment analysis of various TSNAs present in Wiz was done using Molecular Docking and functional annotation approach using David database. Bacterial, fungal and viral species were found in the sample where more than 50 % of total bacterial species were Proteobacteria followed by Actinobacteria, Firmicutes, Apicomplexa, Bacteroidetes, Basidiomycota and Cyanobacteria phyla. Total 22,766 genes with 64.9 % GC content were found, which were associated with nitrogen metabolism, antibiotic resistance, multidrug transportation, multitoxins, and pro-inflammation. The presence of phages in wiz showed the possibility of horizontal gene transfer. The pathway enrichment analysis of various TSNAs of wiz were found to be associated with numerous diseases, such as Parkinson's disease, schizophrenia, mental depression, Hypertension, male infertility, Acute Kidney disease and various kinds of cancers.This study will help to understand the possible role of microbiota, TSNAs and their genetic contribution to the overall product's effect on human health. The raw data was submitted to NCBI Short Read Archive (SRA) under Bio Project accession number PRJNA837388.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation.

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

The nedd-8 activating enzyme gene underlies genetic resistance to infectious pancreatic necrosis virus in Atlantic salmon

Genetic resistance to infectious pancreatic necrosis virus (IPNV) in Atlantic salmon is a rare example of a trait where a single locus (QTL) explains almost all of the genetic variation. Genetic marker tests based on this QTL on salmon chromosome 26 have been widely applied in selective breeding to markedly reduce the incidence of the disease. In the current study, whole genome sequencing and functional annotation approaches were applied to characterise genes and variants in the QTL region. This was complemented by an analysis of differential expression between salmon fry of homozygous resistant and homozygous susceptible genotypes challenged with IPNV. These analyses pointed to the NEDD-8 activating enzyme 1 (nae1) gene as a putative functional candidate underlying the QTL effect. The role of nae1 in IPN resistance was further assessed via CRISPR-Cas9 knockout of the nae1 gene and chemical inhibition of the nae1 protein activity in Atlantic salmon cell lines, both of which resulted in highly significant reduction in productive IPNV replication. In contrast, CRISPR-Cas9 knockout of a candidate gene previously purported to be a cellular receptor for the virus (cdh1) did not have a major impact on productive IPNV replication. These results suggest that nae1 is the causative gene underlying the major QTL affecting resistance to IPNV in salmon, provide further evidence for the critical role of neddylation in host-pathogen interactions, and highlight the value in combining high-throughput genomics approaches with targeted genome editing to understand the genetic basis of disease resistance.

Re-purposing software for functional characterization of the microbiome

BackgroundWidespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit.ResultsTo facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights.ConclusionsWe demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development.CWJ3S1nYSzUqUEQpCfrTiKVideo abstract

A Large-Scale Association Study Detects Novel Rare Variants, Risk Genes, Functional Elements, and Polygenic Architecture of Prostate Cancer Susceptibility.

To identify rare variants associated with prostate cancer susceptibility and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we conducted an integrated study of prostate cancer genetic etiology in two cohorts using custom genotyping microarrays, large imputation reference panels, and functional annotation approaches. Specifically, 11,984 men (6,196 prostate cancer cases and 5,788 controls) of European ancestry from Northern California Kaiser Permanente were genotyped and meta-analyzed with 196,269 men of European ancestry (7,917 prostate cancer cases and 188,352 controls) from the UK Biobank. Three novel loci, including two rare variants (European ancestry minor allele frequency < 0.01, at 3p21.31 and 8p12), were significant genome wide in a meta-analysis. Gene-based rare variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene (ILDR1), which encodes a receptor highly expressed in prostate tissue and is related to the B7/CD28 family of T-cell immune checkpoint markers. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. In addition, a polygenic risk score (PRS) of 188 prostate cancer variants was strongly associated with risk (90th vs. 40th-60th percentile OR = 2.62, P = 2.55 × 10-191). Many of the 188 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a 6-fold difference in log-probability of androgen receptor binding at the variant rs2680708 (17q22). Rare variant and PRS associations, with concomitant functional interpretation of risk mechanisms, can help clarify the full genetic architecture of prostate cancer and other complex traits. SIGNIFICANCE: This study maps the biological relationships between diverse risk factors for prostate cancer, integrating different functional datasets to interpret and model genome-wide data from over 200,000 men with and without prostate cancer.See related commentary by Lachance, p. 1637.

PhANNs, a fast and accurate tool and web server to classify phage structural proteins.

For any given bacteriophage genome or phage-derived sequences in metagenomic data sets, we are unable to assign a function to 50-90% of genes, or more. Structural protein-encoding genes constitute a large fraction of the average phage genome and are among the most divergent and difficult-to-identify genes using homology-based methods. To understand the functions encoded by phages, their contributions to their environments, and to help gauge their utility as potential phage therapy agents, we have developed a new approach to classify phage ORFs into ten major classes of structural proteins or into an "other" category. The resulting tool is named PhANNs (Phage Artificial Neural Networks). We built a database of 538,213 manually curated phage protein sequences that we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering method that ensures there are no homologous proteins between sets yet maintains the maximum sequence diversity for training. An Artificial Neural Network ensemble trained on features extracted from those sets reached a test F1-score of 0.875 and test accuracy of 86.2%. PhANNs can rapidly classify proteins into one of the ten structural classes or, if not predicted to fall in one of the ten classes, as "other," providing a new approach for functional annotation of phage proteins. PhANNs is open source and can be run from our web server or installed locally.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

BackgroundHomology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.ResultsThe application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.ConclusionsWe have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Identification of a functional SNP rs7304782 at schizophrenia risk locus 12q24.31 and validation of its association with schiz ophrenia in Chinese populations

Recent genome-wide association studies (GWAS) have identified multiple schizophrenia-associated risk loci. However, the potential functional (or causal) variant remains largely unknown for each of the identified risk locus. In this study, we utilized different functional annotation approaches (i.e., CADD, Eigen, GWAVA, RegulomeDB and LINSIGHT) to prioritize the most possible functional variant at schizophrenia risk locus 12q24.31, a risk locus that showed genome-wide significant association with schizophrenia. We found that four functional annotation methods prioritized rs7304782 as a potential functional variant at 12q24.31, suggesting the potential functional consequence of rs7304782. Consistent with the functional annotation, reporter gene assays showed that different allele of rs7304782 affected the luciferase activity significantly, further supporting that rs7304782 is a functional variant. We further performed genetic association study and validated that rs7304782 is also associated with schizophrenia in Chinese population (N=4,291 cases and 7,847 controls), with the same risk allele as in European population. Expression quantitative trait loci (eQTL) analysis indicated that rs7304782 was significantly associated with the expression of OGFOD2 in human brain tissues. Of note, differential expression analysis indicated that OGFOD2 was significantly down-regulated in schizophrenia cases compared with controls. Our study identified a potential functional variant (i.e., rs7304782) at schizophrenia risk locus 12q24.31 and suggested that this functional variant may confer schizophrenia risk through regulating OGFOD2 expression.

A New Microbial Pathway for Organophosphonate Degradation Catalyzed by Two Previously Misannotated Non-Heme-Iron Oxygenases.

The assignment of biochemical functions to hypothetical proteins is challenged by functional diversification within many protein structural superfamilies. This diversification, which is particularly common for metalloenzymes, renders functional annotations that are founded solely on sequence and domain similarities unreliable and often erroneous. Definitive biochemical characterization to delineate functional subgroups within these superfamilies will aid in improving bioinformatic approaches for functional annotation. We describe here the structural and functional characterization of two non-heme-iron oxygenases, TmpA and TmpB, which are encoded by a genomically clustered pair of genes found in more than 350 species of bacteria. TmpA and TmpB are functional homologues of a pair of enzymes (PhnY and PhnZ) that degrade 2-aminoethylphosphonate but instead act on its naturally occurring, quaternary ammonium analogue, 2-(trimethylammonio)ethylphosphonate (TMAEP). TmpA, an iron(II)- and 2-(oxo)glutarate-dependent oxygenase misannotated as a γ-butyrobetaine (γbb) hydroxylase, shows no activity toward γbb but efficiently hydroxylates TMAEP. The product, ( R)-1-hydroxy-2-(trimethylammonio)ethylphosphonate [( R)-OH-TMAEP], then serves as the substrate for the second enzyme, TmpB. By contrast to its purported phosphohydrolytic activity, TmpB is an HD-domain oxygenase that uses a mixed-valent diiron cofactor to enact oxidative cleavage of the C-P bond of its substrate, yielding glycine betaine and phosphate. The high specificities of TmpA and TmpB for their N-trimethylated substrates suggest that they have evolved specifically to degrade TMAEP, which was not previously known to be subject to microbial catabolism. This study thus adds to the growing list of known pathways through which microbes break down organophosphonates to harvest phosphorus, carbon, and nitrogen in nutrient-limited niches.

A self-training approach for functional annotation of UniProtKB proteins

A self-training approach for functional annotation of UniProtKB proteins

In Silico Analysis of Heavy Metal Assimilation Behaviors in the Genome of Methanosarcina barkeri str. Fusaro

A computational systems biology representation was utilized for understanding the molecular and metabolic behaviors of heavy metal assimilation system in the genome Methanosarcina barkeri str. Fusaro. A known functional protein of this system was identified by text mining. A combined functional annotation approach was employed to discover the missing proteins with unknown function. Herein, assimilation systems of cadmium, nickel and copper ions have been predicted by using bioinformatics resources. Metabolic flux balances of each pathway model have been optimized for its cellular behaviors in response to excessive heavy metal ions in the environment. Many heavy metals have been utilized by a typical assimilation system on producing the precursors required for methane biosynthesis, and other energy driven processes of this genome. Metal transporter and accessory proteins of methanogens were phylogenetically corresponded with similar proteins in the members of proteobacteria, but metal-containing enzymes are very exclusive to closely related methanogenic archaea. The evolutionary and metabolic relationships of heavy metal transporter system have been observed among archaea and some members in the proteobacteria. Thus, the metabolic models obtained from this study will optimistically be useful in understanding the heavy metal assimilation mechanism of this genome for producing methane, and applying in bioremediation process. Keywords: Cell behavior, heavy metal assimilation, metals detoxification, methanogens, molecular evolution, pathway model, protein function, transporters.

Proteomics and transcriptomics of the BABA-induced resistance response in potato using a novel functional annotation approach.

BackgroundInduced resistance (IR) can be part of a sustainable plant protection strategy against important plant diseases. β-aminobutyric acid (BABA) can induce resistance in a wide range of plants against several types of pathogens, including potato infected with Phytophthora infestans. However, the molecular mechanisms behind this are unclear and seem to be dependent on the system studied. To elucidate the defence responses activated by BABA in potato, a genome-wide transcript microarray analysis in combination with label-free quantitative proteomics analysis of the apoplast secretome were performed two days after treatment of the leaf canopy with BABA at two concentrations, 1 and 10 mM.ResultsOver 5000 transcripts were differentially expressed and over 90 secretome proteins changed in abundance indicating a massive activation of defence mechanisms with 10 mM BABA, the concentration effective against late blight disease. To aid analysis, we present a more comprehensive functional annotation of the microarray probes and gene models by retrieving information from orthologous gene families across 26 sequenced plant genomes. The new annotation provided GO terms to 8616 previously un-annotated probes.ConclusionsBABA at 10 mM affected several processes related to plant hormones and amino acid metabolism. A major accumulation of PR proteins was also evident, and in the mevalonate pathway, genes involved in sterol biosynthesis were down-regulated, whereas several enzymes involved in the sesquiterpene phytoalexin biosynthesis were up-regulated. Interestingly, abscisic acid (ABA) responsive genes were not as clearly regulated by BABA in potato as previously reported in Arabidopsis. Together these findings provide candidates and markers for improved resistance in potato, one of the most important crops in the world.

EggNOG v4.0: nested orthology inference across 3686 organisms

With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.

Bioinformatics approaches for functional annotation of membrane proteins

Membrane proteins perform diverse functions in living organisms such as transporters, receptors and channels. The functions of membrane proteins have been investigated with several computational approaches, such as developing databases, analyzing the structure-function relationship and establishing algorithms to discriminate different type of membrane proteins. However, compilation of bioinformatics resources for the functions of membrane proteins is not well documented compared with their structural aspects. In this comprehensive review, we elaborately focus on three aspects of membrane protein functions: (i) databases for different types of membrane proteins based on their functions including transporters, receptors and ion channels, annotated functional data for genomes, as well as functionally important amino acid residues in membrane proteins obtained from experimental data, (ii) analysis of membrane protein functions based on their structures, motifs, amino acid properties and other features and (iii) algorithms for discriminating different types of membrane proteins and annotating them in genomic sequences. In addition, we provide a list of online resources for the databases and web servers for functional annotation of membrane proteins.

Suppression subtractive hybridization (SSH) combined with bioinformatics method: an integrated functional annotation approach for analysis of differentially expressed immune-genes in insects

The suppression subtractive hybridization (SSH) approach, a PCR based approach which amplifies differentially expressed cDNAs (complementary DNAs), while simultaneously suppressing amplification of common cDNAs, was employed to identify immuneinducible genes in insects. This technique has been used as a suitable tool for experimental identification of novel genes in eukaryotes as well as prokaryotes; whose genomes have been sequenced, or the species whose genomes have yet to be sequenced. In this article, I have proposed a method for in silico functional characterization of immune-inducible genes from insects. Apart from immune-inducible genes from insects, this method can be applied for the analysis of genes from other species, starting from bacteria to plants and animals. This article is provided with a background of SSH-based method taking specific examples from innate immune-inducible genes in insects, and subsequently a bioinformatics pipeline is proposed for functional characterization of newly sequenced genes. The proposed workflow presented here, can also be applied for any newly sequenced species generated from Next Generation Sequencing (NGS) platforms.

Bioinformatic approaches for functional annotation and pathway inference in metagenomics data

Metagenomic approaches are increasingly recognized as a baseline for understanding the ecology and evolution of microbial ecosystems. The development of methods for pathway inference from metagenomics data is of paramount importance to link a phenotype to a cascade of events stemming from a series of connected sets of genes or proteins. Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism, one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires a clear picture of the potentialities of existing tools and development of new tools to characterize, reconstruct and model biochemical and regulatory pathways as the result of integration of function in complex symbiotic interactions of ontologically and evolutionary distinct cell types.

Predicting nucleic acid binding interfaces from structural models of proteins

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure.

The zebrafish:scalable in vivo modeling for systems biology

The zebrafish offers a scalable vertebrate model for many areas of biologic investigation. There is substantial conservation of genetic and genomic features and, at a higher order, conservation of intermolecular networks, as well as physiologic systems and phenotypes. We highlight recent work demonstrating the extent of this homology, and efforts to develop high-throughput phenotyping strategies suited to genetic or chemical screening on a scale compatible with in vivo validation for systems biology. We discuss the implications of these approaches for functional annotation of the genome, elucidation of multicellular processes in vivo, and mechanistic exploration of hypotheses generated by a broad range of 'unbiased' 'omic technologies such as expression profiling and genome-wide association. Finally, we outline potential strategies for the application of the zebrafish to the systematic study of phenotypic architecture, disease heterogeneity and drug responses.

Quantitative comparison of catalytic mechanisms and overall reactions in convergently evolved enzymes: implications for classification of enzyme function.

Functionally analogous enzymes are those that catalyze similar reactions on similar substrates but do not share common ancestry, providing a window on the different structural strategies nature has used to evolve required catalysts. Identification and use of this information to improve reaction classification and computational annotation of enzymes newly discovered in the genome projects would benefit from systematic determination of reaction similarities. Here, we quantified similarity in bond changes for overall reactions and catalytic mechanisms for 95 pairs of functionally analogous enzymes (non-homologous enzymes with identical first three numbers of their EC codes) from the MACiE database. Similarity of overall reactions was computed by comparing the sets of bond changes in the transformations from substrates to products. For similarity of mechanisms, sets of bond changes occurring in each mechanistic step were compared; these similarities were then used to guide global and local alignments of mechanistic steps. Using this metric, only 44% of pairs of functionally analogous enzymes in the dataset had significantly similar overall reactions. For these enzymes, convergence to the same mechanism occurred in 33% of cases, with most pairs having at least one identical mechanistic step. Using our metric, overall reaction similarity serves as an upper bound for mechanistic similarity in functional analogs. For example, the four carbon-oxygen lyases acting on phosphates (EC 4.2.3) show neither significant overall reaction similarity nor significant mechanistic similarity. By contrast, the three carboxylic-ester hydrolases (EC 3.1.1) catalyze overall reactions with identical bond changes and have converged to almost identical mechanisms. The large proportion of enzyme pairs that do not show significant overall reaction similarity (56%) suggests that at least for the functionally analogous enzymes studied here, more stringent criteria could be used to refine definitions of EC sub-subclasses for improved discrimination in their classification of enzyme reactions. The results also indicate that mechanistic convergence of reaction steps is widespread, suggesting that quantitative measurement of mechanistic similarity can inform approaches for functional annotation.

ON THE INFERENCE OF PARSIMONIOUS INDEL EVOLUTIONARY SCENARIOS

Given a multiple alignment of orthologous DNA sequences and a phylogenetic tree for these sequences, we investigate the problem of reconstructing a most parsimonious scenario of insertions and deletions capable of explaining the gaps observed in the alignment. This problem, called the Indel Parsimony Problem, is a crucial component of the problem of ancestral genome reconstruction, and its solution provides valuable information to many genome functional annotation approaches. We first show that the problem is NP-complete. Second, we provide an algorithm, based on the fractional relaxation of an integer linear programming formulation. The algorithm is fast in practice, and the solutions it produces are, in most cases, provably optimal. We describe a divide-and-conquer approach that makes it possible to solve very large instances on a simple desktop machine, while retaining guaranteed optimality. Our algorithms are tested and shown efficient and accurate on a set of 1.8 Mb mammalian orthologous sequences in the CFTR region.