We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes.

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Ab initio Gene Identification in Metagenomic Sequences

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from this is the last candidate. next esc will revert to uncompleted text. he publisher. Overview Dataset shift is a challenging situation where the joint distribution of inputs and outputs differs between the training and test stages. Covariate shift is a simpler particular case of dataset shift where only the input distribution changes (covariate denotes input), while the conditional distribution of the outputs given the inputs p(y|x) remains unchanged. Dataset shift is present in most practical applications for reasons ranging from the bias introduced by experimental design, to the mere irreproducibility of the testing conditions at training time. For example, in an image classification task, training data might have been recorded under controlled laboratory conditions, whereas the test data may show different lighting conditions. In other applications, the process that generates data is in itself adaptive. Some of our authors consider the problem of spam email filtering: successful " spammers " will try to build spam in a form that differs from the spam the automatic filter has been built on. Dataset shift seems to have raised relatively little interest in the machine learning community until very recently. Indeed, many machine learning algorithms are based on the assumption that the training data is drawn from exactly the same distribution as the test data on which the model will later be evaluated. Semi-supervised learning and active learning, two problems that seem very similar to covariate shift have received much more attention. How do they differ from covariate shift? Semi-supervised learning is designed to take advantage of unlabeled data present at training time, but is not conceived to be robust against changes in the input distribution. In fact, one can easily construct examples of covariate shift for which common SSL strategies such as the " cluster assumption " will lead to disaster. In active learning the algorithm is asked to select from the available unlabeled inputs those for which obtaining the label will be most beneficial for learning. This is very relevant in contexts where labeling data is very costly, but active learning strategies 2 Contents are not specifically design for dealing with covariate shift. This book attempts to give an overview of the different recent efforts that are being …

Adaptive computation and machine learning

The seqinR package for the R environment is a library of utilities to retrieve and analyse biological sequences. It provides an interface between i) the R language and environment for statistical computing and graphics and ii) the ACNUC sequence retrieval system for nucleotide and protein sequence databases such as GenBank, EMBL, SWISS-PROT. ACNUC is very efficient in providing direct access to subsequences of biological interest (e.g. protein coding regions, tRNA or rRNA coding regions) present in GenBank and in EMBL. Thanks to a simple query language, it is then easy under R to select sequences of interest and then use all the power of the R environment to analyze them. The ACNUC databases can be locally installed but they are more conveniently accessed through a web server to take advantage of centralized daily updates. The aim of this paper is to provide a handout on basic sequence analyses under seqinR with a special focus on multivariate methods.

SeqinR 1.0-2: A Contributed Package to the R Project for Statistical Computing Devoted to Biological Sequences Retrieval and Analysis

Correlations between genome composition (in terms of GC content) and usage of particular codons and amino acids have been widely reported, but poorly explained. We show here that a simple model of processes acting at the nucleotide level explains codon usage across a large sample of species (311 bacteria, 28 archaea and 257 eukaryotes). The model quantitatively predicts responses (slope and intercept of the regression line on genome GC content) of individual codons and amino acids to genome composition. Codons respond to genome composition on the basis of their GC content relative to their synonyms (explaining 71-87% of the variance in response among the different codons, depending on measure). Amino-acid responses are determined by the mean GC content of their codons (explaining 71-79% of the variance). Similar trends hold for genes within a genome. Position-dependent selection for error minimization explains why individual bases respond differently to directional mutation pressure. Our model suggests that GC content drives codon usage (rather than the converse). It unifies a large body of empirical evidence concerning relationships between GC content and amino-acid or codon usage in disparate systems. The relationship between GC content and codon and amino-acid usage is ahistorical; it is replicated independently in the three domains of living organisms, reinforcing the idea that genes and genomes at mutation/selection equilibrium reproduce a unique relationship between nucleic acid and protein composition. Thus, the model may be useful in predicting amino-acid or nucleotide sequences in poorly characterized taxa.

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A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes.

G:C pairs are more stable than A:T pairs because they have an additional hydrogen bond. This has led to many studies on the correlation between the guanine+cytosine (G+C) content of nucleic acids and temperature over the last 20 years. We collected the optimal growth temperatures (Topt) and the G+C contents of genomic DNA; 23S, 16S, and 5S ribosomal RNAs; and transfer RNAs for 764 prokaryotic species. No correlation was found between genomic G+C content and Topt, but there were striking correlations between the G+C content of ribosomal and transfer RNA stems and Topt. Two explanations have been proposed—neutral evolution and selection pressure—for the approximate equalities of G and C (respectively, A and T) contents within each strand of DNA molecules. Our results do not support the notion that selection pressure induces complementary oligonucleotides in close proximity and therefore numerous secondary structures in prokaryotic DNA, as the genomic G+C content does not behave in the same way as that of folded RNA with respect to optimal growth temperature.

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Relationships Between Genomic G+C Content, RNA Secondary Structures, and Optimal Growth Temperature in Prokaryotes

Separation of B. subtilis DNA into complementary strands. 3. Direct analysis

Separation of B. subtilis DNA into complementary strands, I. Biological properties

In a very receint commulicationl' it was showin that denatured DN'A of B. subtilis can be separated oni a methylated albumini-kieselguhr (MAK) column into two fractionis. These fractions, designated L (light) and H (heavy), were considered to represent the two complementary strands of the native duplex; an attribution based principally on biological properties (bacterial transformation) as well as on a few physicochemical criteria. Additional evidence in support of this view will be presented in this study, which shows that the polyribonlucleotides synthesized enizymically with the separated fractions as the templates exhibit a mutually complementary base composition. The discussion will also offer the opportunity of pointing out an uinexpected compositionial regularity evideniced by the single strands. Previous work in m'any laboratories2" as well as in this laboratory7-9 has indicated that the polyribonucleotide product of the action of RNA polymerase is a more or less faithful complementary copy of the DNA serving as the template. Since it was knowni that denatured templates could be copied without the alteration of the expected base ratios of the product,7' 9 it was of interest to deduce the base compositiont of the separated DNA strands from the anialysis of the polyribonucleotides to which they gave rise. Because of the great sensitivity of the method, only about 30 Ag of DNA are required for a complete analysis in triplicate.

Seapration of B. subtilis DNA into complementary strands. II. Template functions and composition as determined by transcription with RNA polymerase.

Studies on the nucleotide arrangement in deoxyribonucleic acids. X. Frequency and composition of pyrimidine isostichs in microbial deoxyribonucleic acids and in the DNA of E. coli phage T3.

DNA preparations from seven bacterial species and from the E. coli phage T4 can, after denaturation with alkali, be separated chromatographically into two distinct components (L and H) through intermittent gradient elution from methylated albumin kieselguhr columns. The direct chemical analysis of the L and H fractions isolated from DNA specimens of the AT type shows them to exhibit a high degree of complementarity; but despite a bias in the distribution of purines and pyrimidines, either fraction contains equimolar quantities of 6-amino and of 6-keto nucleotides. In the L and H components derived from DNA of the equimolar and GC types, the distribution bias appears limited to guanine and cytosine. It is suggested that the L and H fractions represent the complementary DNA strands.

Rivka Rudner

Papers

Separation of B. subtilis DNA into complementary strands. 3. Direct analysis

Separation of B. subtilis DNA into complementary strands, I. Biological properties

Seapration of B. subtilis DNA into complementary strands. II. Template functions and composition as determined by transcription with RNA polymerase.

Studies on the nucleotide arrangement in deoxyribonucleic acids. X. Frequency and composition of pyrimidine isostichs in microbial deoxyribonucleic acids and in the DNA of E. coli phage T3.

Separation of microbial deoxyribonucleic acids into complementary strands