Thousands Of Loci Research Articles

A rare-variant test for high-dimensional data

Genome-wide association studies have facilitated the discovery of thousands of loci for hundreds of phenotypes. However, the issue of missing heritability remains unsolved for most complex traits. Locus discovery could be enhanced with both improved power through multi-phenotype analysis (MPA) and use of a wider allele frequency range, including rare variants (RVs). MPA methods for single-variant association have been proposed, but given their low power for RVs, more efficient approaches are required. We propose multi-phenotype analysis of rare variants (MARV), a burden test-based method for RVs extended to the joint analysis of multiple phenotypes through a powerful reverse regression technique. Specifically, MARV models the proportion of RVs at which minor alleles are carried by individuals within a genomic region as a linear combination of multiple phenotypes, which can be both binary and continuous, and the method accommodates directly the genotyped and imputed data. The full model, including all phenotypes, is tested for association for discovery, and a more thorough dissection of the phenotype combinations for any set of RVs is also enabled. We show, via simulations, that the type I error rate is well controlled under various correlations between two continuous phenotypes, and that the method outperforms a univariate burden test in all considered scenarios. Application of MARV to 4876 individuals from the Northern Finland Birth Cohort 1966 for triglycerides, high- and low-density lipoprotein cholesterols highlights known loci with stronger signals of association than those observed in univariate RV analyses and suggests novel RV effects for these lipid traits.

Non-linear patterns in age-related DNA methylation may reflect CD4+ T cell differentiation

ABSTRACTDNA methylation (DNAm) is an important epigenetic process involved in the regulation of gene expression. While many studies have identified thousands of loci associated with age, few have differentiated between linear and non-linear DNAm trends with age. Non-linear trends could indicate early- or late-life gene regulatory processes. Using data from the Illumina 450K array on 336 human peripheral blood samples, we identified 21 CpG sites that associated with age (P<1.03E-7) and exhibited changing rates of DNAm change with age (P<1.94E-6). For 2 of these CpG sites (cg07955995 and cg22285878), DNAm increased with age at an increasing rate, indicating that differential DNAm was greatest among elderly individuals. We observed significant replication for both CpG sites (P<5.0E-8) in a second set of peripheral blood samples. In 8 of 9 additional data sets comprising samples of monocytes, T cell subtypes, and brain tissue, we observed a pattern directionally consistent with DNAm increasing with age at an increasing rate, which was nominally significant in the 3 largest data sets (4.3E-15<P<0.039). cg07955995 and cg22285878 reside in the promoter region of KLF14, which encodes a protein involved in immune cell differentiation via the repression of FOXP3. These findings may suggest a possible role for cg07955995 and cg22285878 in immunosenescence.

The impact of GC bias on phylogenetic accuracy using targeted enrichment phylogenomic data

The field of sequence based phylogenetic analyses is currently being transformed by novel hybrid-based targeted enrichment methods, such as the use of ultraconserved elements (UCEs). Rather than analyzing relationships among organisms using a small number of genes, these methods now allow us to evaluate relationships with many hundreds to thousands of individual gene loci. However, the inclusion of thousands of loci does not necessarily overcome the long-standing challenge of incongruence among phylogenetic trees derived from different genes or gene regions. One factor that impacts the level of incongruence in phylogenomic data sets is the level of GC bias. GC rich gene regions are prone to higher recombination rates than AT rich regions, driven by a process referred to as “GC biased gene conversion”. As a result, high GC content can be negatively associated with phylogenetic accuracy, but the extent to which this impacts incongruence among UCEs is currently unstudied. We investigated the impact of GC content on phylogeny reconstruction using in silico captured UCE data for the corbiculate bees (Hymenoptera: Apidae). The phylogeny of this group has been the subject of extensive study, and incongruence among gene trees is thought to be a source of phylogenetic error. We conducted coalescent- and concatenation-based analyses of 810 individual gene loci from all 13 currently available bee genomes, including 8 corbiculate taxa. Both coalescent- and concatenation-based methods converged on a single topology for the corbiculate tribes. In contrast to concatenation, the coalescent-based methods revealed significant topological conflict at nodes involving the orchid bees (Euglossini) and honeybees (Apini). Partitioning the loci by GC content reveals decreasing support for the inferred topology with increasing GC bias. Based on the results of this study, we report the first evidence that GC biased gene conversion may contribute to topological incongruence in studies based on ultraconserved elements.

Cell Type-Specific Epigenomic Analysis Reveals a Uniquely Closed Chromatin Architecture in Mouse Rod Photoreceptors

Rod photoreceptors are specialized neurons that mediate vision in dim light and are the predominant photoreceptor type in nocturnal mammals. The rods of nocturnal mammals are unique among vertebrate cell types in having an ‘inverted’ nuclear architecture, with a dense mass of heterochromatin in the center of the nucleus rather than dispersed clumps at the periphery. To test if this unique nuclear architecture is correlated with a unique epigenomic landscape, we performed ATAC-seq on mouse rods and their most closely related cell type, cone photoreceptors. We find that thousands of loci are selectively closed in rods relative to cones as well as >60 additional cell types. Furthermore, we find that the open chromatin profile of photoreceptors lacking the rod master regulator Nrl is nearly indistinguishable from that of native cones, indicating that Nrl is required for selective chromatin closure in rods. Finally, we identified distinct enrichments of transcription factor binding sites in rods and cones, revealing key differences in the cis-regulatory grammar of these cell types. Taken together, these data provide insight into the development and maintenance of photoreceptor identity, and highlight rods as an attractive system for studying the relationship between nuclear organization and local changes in gene regulation.

MARV: a tool for genome-wide multi-phenotype analysis of rare variants

BackgroundGenome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.ResultsWe propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (PTG+FI = 1.8 × 10−9) than observed in single phenotype rare variant analyses (PTG = 6.5 × 10−8 and PFI = 0.27).ConclusionsMARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenotypes, thus paving the way for novel discoveries and insights into biology of complex traits.

Correction: Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation

[This corrects the article DOI: 10.1371/journal.pgen.1005804.].

Not All H3K4 Methylations Are Created Equal: Mll2/COMPASS Dependency in Primordial Germ Cell Specification

The spatiotemporal regulation of gene expression is central for cell-lineage specification during embryonic development and is achieved through the combinatorial action of transcription factors/co-factors and epigenetic states at cis-regulatory elements. Here, we show that in addition to implementing H3K4me3 at promoters of bivalent genes, Mll2 (KMT2B)/COMPASS can also implement H3K4me3 at a subset of non-TSS regulatory elements, a subset of which shares epigenetic signatures of active enhancers. Our mechanistic studies reveal that association of Mll2's CXXC domain with CpG-rich regions plays aninstrumental role for chromatin targeting and subsequent implementation of H3K4me3. Although Mll2/COMPASS is required for H3K4me3 implementation on thousands of loci, generation of catalytically mutant MLL2/COMPASS demonstrated that H3K4me3 implemented by this enzyme was essential for expression of a subset of genes, including those functioning in the control of transcriptional programs during embryonic development. Our findings suggest that not all H3K4 trimethylations implemented by MLL2/COMPASS are functionally equivalent.

Fungal Phylogeny in the Age of Genomics: Insights Into Phylogenetic Inference From Genome-Scale Datasets.

The genomic era has been transformative for many fields, including our understanding of the phylogenetic relationships between organisms. The wide availability of whole-genome sequences practically eliminated data availability as a limiting factor for inferring phylogenetic trees, providing hundreds to thousands of loci for analyses, leading to molecular phylogenetics gradually being replaced by phylogenomics. The new era has also brought new challenges: systematic errors (resulting from, e.g., model violation) can be more pronounced in phylogenomic datasets and can lead to strongly supported incorrect relationships, creating significant incongruence among studies. Here, we review common practices, technical and biological challenges of phylogenomic analyses, with examples illustrated from fungi. We compare major approaches of phylogenetic inference, and illustrate the advantages conferred and challenges presented in phylogenomic case studies across the fungal tree of life, including cases where genome-scale data could conclusively resolve contentious relationships, and others that remain challenging despite the flood of genomic data.

Challenges in Species Tree Estimation Under the Multispecies Coalescent Model.

The multispecies coalescent (MSC) model has emerged as a powerful framework for inferring species phylogenies while accounting for ancestral polymorphism and gene tree-species tree conflict. A number of methods have been developed in the past few years to estimate the species tree under the MSC. The full likelihood methods (including maximum likelihood and Bayesian inference) average over the unknown gene trees and accommodate their uncertainties properly but involve intensive computation. The approximate or summary coalescent methods are computationally fast and are applicable to genomic datasets with thousands of loci, but do not make an efficient use of information in the multilocus data. Most of them take the two-step approach of reconstructing the gene trees for multiple loci by phylogenetic methods and then treating the estimated gene trees as observed data, without accounting for their uncertainties appropriately. In this article we review the statistical nature of the species tree estimation problem under the MSC, and explore the conceptual issues and challenges of species tree estimation by focusing mainly on simple cases of three or four closely related species. We use mathematical analysis and computer simulation to demonstrate that large differences in statistical performance may exist between the two classes of methods. We illustrate that several counterintuitive behaviors may occur with the summary methods but they are due to inefficient use of information in the data by summary methods and vanish when the data are analyzed using full-likelihood methods. These include (i) unidentifiability of parameters in the model, (ii) inconsistency in the so-called anomaly zone, (iii) singularity on the likelihood surface, and (iv) deterioration of performance upon addition of more data. We discuss the challenges and strategies of species tree inference for distantly related species when the molecular clock is violated, and highlight the need for improving the computational efficiency and model realism of the likelihood methods as well as the statistical efficiency of the summary methods.

The functions of long noncoding RNAs in development and stem cells.

Eukaryotic genomes are pervasively transcribed, with tens of thousands of RNAs emanating from uni- and bi-directional promoters and from active enhancers. In vertebrates, thousands of loci in each species produce a class of transcripts called long noncoding RNAs (lncRNAs) that are typically expressed at low levels and do not appear to give rise to functional proteins. Substantial numbers of lncRNAs are expressed at specific stages of embryonic development, in many cases from regions flanking key developmental regulators. Here, we review the known biological functions of such lncRNAs and the emerging paradigms of their modes of action. We also provide an overview of the growing arsenal of methods for lncRNA identification, perturbation and functional characterization.

Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci.

Contemporary effective population size (Ne) can be estimated using linkage disequilibrium (LD) observed across pairs of loci presumed to be selectively neutral and unlinked. This method has been commonly applied to data sets containing 10-100 loci to inform conservation and study population demography. Performance of these Ne estimates could be improved by incorporating data from thousands of loci. However, these thousands of loci exist on a limited number of chromosomes, ensuring that some fraction will be physically linked. Linked loci have elevated LD due to limited recombination, which if not accounted for can cause Ne estimates to be downwardly biased. Here, we present results from coalescent and forward simulations designed to evaluate the bias of LD-based Ne estimates ([Ncirc ]e). Contrary to common perceptions, increasing the number of loci does not increase the magnitude of linkage. Although we show it is possible to identify some pairs of loci that produce unusually large r(2) values, simply removing large r(2) values is not a reliable way to eliminate bias. Fortunately, the magnitude of bias in [Ncirc ]e is strongly and negatively correlated with the process of recombination, including the number of chromosomes and their length, and this relationship provides a general way to adjust for bias. Additionally, we show that with thousands of loci, precision of [Ncirc ]e is much lower than expected based on the assumption that each pair of loci provides completely independent information.

RADcap: sequence capture of dual‐digest RADseq libraries with identifiable duplicates and reduced missing data

Molecular ecologists seek to genotype hundreds to thousands of loci from hundreds to thousands of individuals at minimal cost per sample. Current methods, such as restriction-site-associated DNA sequencing (RADseq) and sequence capture, are constrained by costs associated with inefficient use of sequencing data and sample preparation. Here, we introduce RADcap, an approach that combines the major benefits of RADseq (low cost with specific start positions) with those of sequence capture (repeatable sequencing of specific loci) to significantly increase efficiency and reduce costs relative to current approaches. RADcap uses a new version of dual-digest RADseq (3RAD) to identify candidate SNP loci for capture bait design and subsequently uses custom sequence capture baits to consistently enrich candidate SNP loci across many individuals. We combined this approach with a new library preparation method for identifying and removing PCR duplicates from 3RAD libraries, which allows researchers to process RADseq data using traditional pipelines, and we tested the RADcap method by genotyping sets of 96-384 Wisteria plants. Our results demonstrate that our RADcap method: (i) methodologically reduces (to <5%) and allows computational removal of PCR duplicate reads from data, (ii) achieves 80-90% reads on target in 11 of 12 enrichments, (iii) returns consistent coverage (≥4×) across >90% of individuals at up to 99.8% of the targeted loci, (iv) produces consistently high occupancy matrices of genotypes across hundreds of individuals and (v) costs significantly less than current approaches.

Maximum Likelihood Implementation of an Isolation-with-Migration Model for Three Species.

We develop a maximum likelihood (ML) method for estimating migration rates between species using genomic sequence data. A species tree is used to accommodate the phylogenetic relationships among three species, allowing for migration between the two sister species, while the third species is used as an out-group. A Markov chain characterization of the genealogical process of coalescence and migration is used to integrate out the migration histories at each locus analytically, whereas Gaussian quadrature is used to integrate over the coalescent times on each genealogical tree numerically. This is an extension of our early implementation of the symmetrical isolation-with-migration model for three species to accommodate arbitrary loci with two or three sequences per locus and to allow asymmetrical migration rates. Our implementation can accommodate tens of thousands of loci, making it feasible to analyze genome-scale data sets to test for gene flow. We calculate the posterior probabilities of gene trees at individual loci to identify genomic regions that are likely to have been transferred between species due to gene flow. We conduct a simulation study to examine the statistical properties of the likelihood ratio test for gene flow between the two in-group species and of the ML estimates of model parameters such as the migration rate. Inclusion of data from a third out-group species is found to increase dramatically the power of the test and the precision of parameter estimation. We compiled and analyzed several genomic data sets from the Drosophila fruit flies. Our analyses suggest no migration from D. melanogaster to D. simulans, and a significant amount of gene flow from D. simulans to D. melanogaster, at the rate of ~0.02 migrant individuals per generation. We discuss the utility of the multispecies coalescent model for species tree estimation, accounting for incomplete lineage sorting and migration.

Ddradseqtools: a software package for in silico simulation and testing of double-digest RADseq experiments.

Double-digested RADseq (ddRADseq) is a NGS methodology that generates reads from thousands of loci targeted by restriction enzyme cut sites, across multiple individuals. To be statistically sound and economically optimal, a ddRADseq experiment has a preliminary design stage that needs to consider issues related to the selection of enzymes, particular features of the genome of the focal species, possible modifications to the library construction protocol, coverage needed to minimize missing data, and the potential sources of error that may impact upon the coverage. We present ddradseqtools, a software package to help ddRADseq experimental design by (i) the generation of in silico double-digested fragments; (ii) the construction of modified ddRADseq libraries using adapters with either one or two indexes and degenerate base regions (DBRs) to quantify PCR duplicates; and (iii) the initial steps of the bioinformatics preprocessing of reads. ddradseqtools generates single-end (SE) or paired-end (PE) reads that may bear SNPs and/or indels. The effect of allele dropout and PCR duplicates on coverage is also simulated. The resulting output files can be submitted to pipelines of alignment and variant calling, to allow the fine-tuning of parameters. The software was validated with specific tests for the correct operability of the program. The correspondence between in silico settings and parameters from ddRADseq invitro experiments was assessed to provide guidelines for the reliable performance of the software. ddradseqtools is cost-efficient in terms of execution time, and can be run on computers with standard CPU and RAM configuration.

Cryptic Genetic Variation in Evolutionary Developmental Genetics.

Evolutionary developmental genetics has traditionally been conducted by two groups: Molecular evolutionists who emphasize divergence between species or higher taxa, and quantitative geneticists who study variation within species. Neither approach really comes to grips with the complexities of evolutionary transitions, particularly in light of the realization from genome-wide association studies that most complex traits fit an infinitesimal architecture, being influenced by thousands of loci. This paper discusses robustness, plasticity and lability, phenomena that we argue potentiate major evolutionary changes and provide a bridge between the conceptual treatments of macro- and micro-evolution. We offer cryptic genetic variation and conditional neutrality as mechanisms by which standing genetic variation can lead to developmental system drift and, sheltered within canalized processes, may facilitate developmental transitions and the evolution of novelty. Synthesis of the two dominant perspectives will require recognition that adaptation, divergence, drift and stability all depend on similar underlying quantitative genetic processes—processes that cannot be fully observed in continuously varying visible traits.

Sequence Capture versus Restriction Site Associated DNA Sequencing for Shallow Systematics

Sequence capture and restriction site associated DNA sequencing (RAD-Seq) are two genomic enrichment strategies for applying next-generation sequencing technologies to systematics studies. At shallow timescales, such as within species, RAD-Seq has been widely adopted among researchers, although there has been little discussion of the potential limitations and benefits of RAD-Seq and sequence capture. We discuss a series of issues that may impact the utility of sequence capture and RAD-Seq data for shallow systematics in non-model species. We review prior studies that used both methods, and investigate differences between the methods by re-analyzing existing RAD-Seq and sequence capture data sets from a Neotropical bird (Xenops minutus). We suggest that the strengths of RAD-Seq data sets for shallow systematics are the wide dispersion of markers across the genome, the relative ease and cost of laboratory work, the deep coverage and read overlap at recovered loci, and the high overall information that results. Sequence capture's benefits include flexibility and repeatability in the genomic regions targeted, success using low-quality samples, more straightforward read orthology assessment, and higher per-locus information content. The utility of a method in systematics, however, rests not only on its performance within a study, but on the comparability of data sets and inferences with those of prior work. In RAD-Seq data sets, comparability is compromised by low overlap of orthologous markers across species and the sensitivity of genetic diversity in a data set to an interaction between the level of natural heterozygosity in the samples examined and the parameters used for orthology assessment. In contrast, sequence capture of conserved genomic regions permits interrogation of the same loci across divergent species, which is preferable for maintaining comparability among data sets and studies for the purpose of drawing general conclusions about the impact of historical processes across biotas. We argue that sequence capture should be given greater attention as a method of obtaining data for studies in shallow systematics and comparative phylogeography.

Ecological population genomics in the marine environment.

Marine species live in a wide diversity of environments and yet, because of their pelagic life stages, are thought to be well-connected: they have high migration rates that inhibit significant population structure. Recent innovations in sequencing technologies now provide information on nucleotide polymorphisms at thousands to tens of thousands of loci based on whole genomes, reduced representative portions of genomes (0.1-1%) or a majority of expressed mRNAs. Data from these genomic approaches are used to define and quantify single-nucleotide polymorphisms (SNPs). These SNP data tend to agree with data from older technologies (allozymes or microsatellites), which support well-connected populations with few genetic differences among populations. However, these studies also find few percentages of SNPs (1-5%) that readily distinguish genetic differences among populations on relatively small geographic scales. The magnitudes of the genetic differences (FST values) suggest that hundreds of loci with significant differences are due to positive selective pressures. Thus, these data suggest that natural selection is effectively altering allele frequencies at 100s of loci in marine populations. In this manuscript, we provide examples of these studies, the strengths and weaknesses of different genomic approaches as well as important technical aspects associated with genomic approaches.

Will there ever be a tree of life that systematists can agree on?

Since the concept of a “Tree of Life” was raised by Charles Darwin, researches in this field have not only contributed to our understanding of phylogenetic relationships among taxa, but also significantly accelerated the development of related subjects in biological science. Evolutionary biologist Dobzhansky once remarked that “nothing makes sense in biology except in the light of evolution”, which has been largely echoed by later biologists. Indeed, reconstruction of an accurate phylogeny of the living world is very important for biological classification and nomenclature, and also crucial to elucidate the origin and diversification of life. We have experienced three major phases for Tree of Life reconstruction in the past century. Prior to the 1990s, taxonomists published classification systems that were largely dependent on morphological characters. DNA sequencing technology facilitated by the development of polymerase chain reaction (PCR) techniques has allowed systematists to reconstruct phylogenetic relationships using molecular data. More recently, the rapid development of next-generation sequencing tools has brought the Tree of Life to a phylogenomic era by enabling the construction of phylogenies using hundreds or thousands of loci from organellar and nuclear genomes. However, significant conflicts have been detected in phylogenies of various organisms with the large increase in the number of loci used for phylogenetic analyses. Given the level of conflict in some data sets, some researchers have begun to doubt the accuracy and congruence of the Tree of Life and its applications in related biological fields. So, will there ever be a Tree of Life that systematists can agree on?

Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation.

Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors.

AMAS: a fast tool for alignment manipulation and computing of summary statistics.

The amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa. These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. Computationally efficient tools for handling and computing properties of thousands of single-locus or large concatenated alignments are needed. Here I present AMAS (Alignment Manipulation And Summary), a tool that can be used either as a stand-alone command-line utility or as a Python package. AMAS works on amino acid and nucleotide alignments and combines capabilities of sequence manipulation with a function that calculates basic statistics. The manipulation functions include conversions among popular formats, concatenation, extracting sites and splitting according to a pre-defined partitioning scheme, creation of replicate data sets, and removal of taxa. The statistics calculated include the number of taxa, alignment length, total count of matrix cells, overall number of undetermined characters, percent of missing data, AT and GC contents (for DNA alignments), count and proportion of variable sites, count and proportion of parsimony informative sites, and counts of all characters relevant for a nucleotide or amino acid alphabet. AMAS is particularly suitable for very large alignments with hundreds of taxa and thousands of loci. It is computationally efficient, utilizes parallel processing, and performs better at concatenation than other popular tools. AMAS is a Python 3 program that relies solely on Python’s core modules and needs no additional dependencies. AMAS source code and manual can be downloaded from http://github.com/marekborowiec/AMAS/ under GNU General Public License.