Dataset Information

Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

ABSTRACT: Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or more on a typical server). Here, we present xBASE-Orth, a system for ongoing ortholog annotation, which applies a "divide and conquer" approach and adopts a pragmatic scheme that trades accuracy for speed. Starting at species level, xBASE-Orth carefully constructs and uses pan-genomes as proxies for the full collections of coding sequences at each level as it progressively climbs the taxonomic tree using the previously computed data. This leads to a significant decrease in the number of alignments that need to be performed, which translates into faster computation, making ortholog computation possible on a global scale. Using xBASE-Orth, we analyzed an NCBI collection of 1,288 bacterial and 94 archaeal complete genomes with more than 4 million coding sequences in 5 weeks and predicted more than 700 million ortholog pairs, clustered in 175,531 orthologous groups. We have also identified sets of highly conserved bacterial and archaeal orthologs and in so doing have highlighted anomalies in genome annotation and in the proposed composition of the minimal bacterial genome. In summary, our approach allows for scalable and efficient computation of the bacterial and archaeal ortholog annotations. In addition, due to its hierarchical nature, it is suitable for incorporating novel complete genomes and alternative genome annotations. The computed ortholog data and a continuously evolving set of applications based on it are integrated in the xBASE database, available at http://www.xbase.ac.uk/.

SUBMITTER: Halachev MR

PROVIDER: S-EPMC3236195 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

Halachev Mihail R MR Loman Nicholas J NJ Pallen Mark J MJ

PloS one 20111212 12

Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or mo ...[more]

PMID: 22174796

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Project description:BackgroundThe recent rise in cultivation-independent genome sequencing has provided key material to explore uncharted branches of the Tree of Life. This has been particularly spectacular concerning the Archaea, projecting them at the center stage as prominently relevant to understand early stages in evolution and the emergence of fundamental metabolisms as well as the origin of eukaryotes. Yet, resolving deep divergences remains a challenging task due to well-known tree-reconstruction artefacts and biases in extracting robust ancient phylogenetic signal, notably when analyzing data sets including the three Domains of Life. Among the various strategies aimed at mitigating these problems, divide-and-conquer approaches remain poorly explored, and have been primarily based on reconciliation among single gene trees which however notoriously lack ancient phylogenetic signal.ResultsWe analyzed sub-sets of full supermatrices covering the whole Tree of Life with specific taxonomic sampling to robustly resolve different parts of the archaeal phylogeny in light of their current diversity. Our results strongly support the existence and early emergence of two main clades, Cluster I and Cluster II, which we name Ouranosarchaea and Gaiarchaea, and we clarify the placement of important novel archaeal lineages within these two clades. However, the monophyly and branching of the fast evolving nanosized DPANN members remains unclear and worth of further study.ConclusionsWe inferred a well resolved rooted phylogeny of the Archaea that includes all recently described phyla of high taxonomic rank. This phylogeny represents a valuable reference to study the evolutionary events associated to the early steps of the diversification of the archaeal domain. Beyond the specifics of archaeal phylogeny, our results demonstrate the power of divide-and-conquer approaches to resolve deep phylogenetic relationships, which should be applied to progressively resolve the entire Tree of Life.

Project description:Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10-3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping.

Dataset Information

Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

Publications

Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

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