Demography-adjusted tests of neutrality based on genome-wide SNP data
Marina Rafajlović (1), Alexander Klassmann (2), Anders Eriksson (3), Thomas Wiehe (2), Bernhard Mehlig (1) ((1) Department of Physics, University of Gothenburg, Sweden, (2) Institut für Genetik, Universität zu Köln, Germany, (3) Department of Zoology, University of Cambridge, U.K.)
(Submitted on 1 Jul 2013)

Tests of the neutral evolution hypothesis are usually built on the standard null model which assumes that mutations are neutral and population size remains constant over time. However, it is unclear how such tests are affected if the last assumption is dropped. Here, we extend the unifying framework for tests based on the site frequency spectrum, introduced by Achaz and Ferretti, to populations of varying size. A key ingredient is to specify the first two moments of the frequency spectrum. We show that these moments can be determined analytically if a population has experienced two instantaneous size changes in the past. We apply our method to data from ten human populations gathered in the 1000 genomes project, estimate their demographies and define demography-adjusted versions of Tajima’s $D$, Fay & Wu’s $H$, and Zeng’s $E$. The adjusted test statistics facilitate the direct comparison between populations and they show that most of the differences among populations seen in the original tests can be explained by demography. We carried out whole genome screens for deviation from neutrality and identified candidate regions of recent positive selection. We provide track files with values of the adjusted and original tests for upload to the UCSC genome browser.

Lateral Gene Transfer, Rearrangement and Reconciliation
Murray Patterson, Gergely J Szöllősi, Vincent Daubin, Eric Tannier
(Submitted on 27 Jun 2013)

Background.
Models of ancestral gene order reconstruction have progressively integrated different evolutionary patterns and processes such as unequal gene content, gene duplications, and implicitly sequence evolution via reconciled gene trees. In unicellular organisms, these models have so far ignored lateral gene transfer, even though it can have an important confounding effect on such models, as well as a rich source of information on the function of genes through the detection of transfers of entire clusters of genes.
Result.
We report an algorithm together with its implementation, DeCoLT, that reconstructs ancestral genome organization based on reconciled gene trees which summarize information on sequence evolution, gene origination, duplication, loss, and lateral transfer. DeCoLT finds in polynomial time the minimum number of rearrangements, computed as the number of gains and breakages of adjacencies between pairs of genes. We apply DeCoLT to 1099 gene families from 36 cyanobacteria genomes.
Conclusion.
DeCoLT is able to reconstruct adjacencies in 35 ancestral bacterial genomes with a thousand genes families in a few hours, and detects clusters of co-transferred genes. As there is no constraint on genome organization, adjacencies can be generalized to any relationship between genes to reconstruct ancestral interactions, functions or complexes with the same framework.

mendelFix: a Perl script for checking Mendelian errors in high density SNP data of trio designs
Yuri Tani Utsunomiya, Rodrigo Vitorio Alonso, Adriana Santana do Carmo, Francine Campagnari, José Antonio Vinsintin, José Fernando Garcia
(Submitted on 10 Jun 2013)

Here we present mendelFix, a Perl script for checking Mendelian errors in genome-wide SNP data of trio designs. The program takes 12-recoded PLINK PED and MAP files as input to calculate a series of summary statistics for Mendelian errors, sets missing offspring genotypes that present Mendelian inconsistencies, and implements a simplistic procedure to infer missing genotypes using parent information. The program can be easily incorporated in any pipeline for family-based SNP data analysis, and is distributed as free software under the GNU General Public License.

Enhancement of a Novel Method for Mutational Disease Prediction using Bioinformatics Techniques and Backpropagation Algorithm
Ayad Ghany Ismaeel, Anar Auda Ablahad
(Submitted on 7 Jun 2013)

The noval method for mutational disease prediction using bioinformatics tools and datasets for diagnosis the malignant mutations with powerful Artificial Neural Network (Backpropagation Network) for classifying these malignant mutations are related to gene(s) (like BRCA1 and BRCA2) cause a disease (breast cancer). This noval method did not take in consideration just like adopted for dealing, analyzing and treat the gene sequences for extracting useful information from the sequence, also exceeded the environment factors which play important roles in deciding and calculating some of genes features in order to view its functional parts and relations to diseases. This paper is proposed an enhancement of a novel method as a first way for diagnosis and prediction the disease by mutations considering and introducing multi other features show the alternations, changes in the environment as well as genes, comparing sequences to gain information about the structure or function of a query sequence, also proposing optimal and more accurate system for classification and dealing with specific disorder using backpropagation with mean square rate 0.000000001. Index Terms (Homology sequence, GC content and AT content, Bioinformatics, Backpropagation Network, BLAST, DNA Sequence, Protein Sequence)

Computing the posterior expectation of phylogenetic trees
Philipp Benner, Miroslav Bačák
(Submitted on 16 May 2013)

Inferring phylogenetic trees from multiple sequence alignments often relies upon Markov chain Monte Carlo (MCMC) methods to generate tree samples from a posterior distribution. To give a rigorous approximation of the posterior expectation, one needs to compute the mean of the tree samples and therefore a sound definition of a mean and algorithms for its computation are highly demanded. To the best of our knowledge, no existing method of phylogenetic inference can handle the full set of sample trees, because such trees typically have different topologies. We develop a novel statistical model for the inference of phylogenetic trees based on the tree space due to Billera et al. [2001]. Since it is an Hadamard space, the mean and median are well defined, which we also motivate from a decision theoretic perspective. The actual approximation of the posterior expectation relies on some recent developments in Hadamard spaces (Ba\v{c}\’ak [2013a], Miller et al. [2012]) and the fast computation of geodesics in tree space (Owen and Provan [2011]), which altogether enable to compute medians and means of trees with different topologies. Our intention is to give a full self-contained description of the methods required to approximate posterior expectations. We demonstrate these methods on the small ribosomal subunit rRNA sequence alignment. The posterior expectations obtained on this data set are a meaningful summary of the posterior distribution and the uncertainty about the tree topology.