Super paramagnetic clustering of protein sequences

Tetko, Igor V.; Facius, Axel; Ruepp, Andreas; Mewes, Hans‐Werner

doi:10.1186/1471-2105-6-82

Cited by 35 publications

(13 citation statements)

References 32 publications

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“…SPC is based on computing the K -nearest neighborhood to produce a cluster relationship matrix and a hierarchical tree of clusters. Specifically, SPC is implemented as Sorting Points Into Neighborhood (SPIN) [ 69 , 70 ], which employs the Potts Hamiltonian model [ 71 ] to identify the number and size of clusters, known as cluster stability. The problem with SPC is how to specify the number of the nearest neighborhood an individual can have.…”

Section: Nonparametric Approachesmentioning

confidence: 99%

Nonparametric approaches for population structure analysis

Al-Husain

Hafez

2018

Hum Genomics

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The analysis of population structure has many applications in medical and population genetic research. Such analysis is used to provide clear insight into the underlying genetic population substructure and is a crucial prerequisite for any analysis of genetic data. The analysis involves grouping individuals into subpopulations based on shared genetic variations. The most widely used markers to study the variation of DNA sequences between populations are single nucleotide polymorphisms. Data preprocessing is a necessary step to assess the quality of the data and to determine which markers or individuals can reasonably be included in the analysis. After preprocessing, several methods can be utilized to uncover population substructure, which can be categorized into two broad approaches: parametric and nonparametric. Parametric approaches use statistical models to infer population structure and assign individuals into subpopulations. However, these approaches suffer from many drawbacks that make them impractical for large datasets. In contrast, nonparametric approaches do not suffer from these drawbacks, making them more viable than parametric approaches for analyzing large datasets. Consequently, nonparametric approaches are increasingly used to reveal population substructure. Thus, this paper reviews and discusses the nonparametric approaches that are available for population structure analysis along with some implications to resolve challenges.

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Section: Nonparametric Approachesmentioning

confidence: 99%

Nonparametric approaches for population structure analysis

Al-Husain

Hafez

2018

Hum Genomics

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“…To determine the uppermost hierarchical population structure, we applied an unsupervised network-based clustering algorithm called super paramagnetic clustering (SPC, Blatt et al, 1996;Tetko et al, 2005). The input to SPC is a dissimilarity matrix D (n × n) with pairwise genetic distances between all individuals calculated using allele sharing distance (ASD; one minus IBS).…”

Section: Hierarchical Population Structurementioning

confidence: 99%

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

et al. 2016

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Pollination is a key ecosystem service for agricultural systems and Western honey bees, Apis mellifera, are the most important managed pollinators. Major losses of managed honey bee colonies reinforced the need to take advantage of locally adapted subspecies and ecotypes to buffer populations against various stressors. However, introductions of non-native honey bees from distant lineages are likely to undermine respective conservation efforts unless reliable and cost effective tools can be used to identify hybridization. The purpose of this study is to characterize current population structure and genetic diversity, and to assess the degree of admixture between native and introduced honey bees. Moreover, we aim to select a reduced number of genetic markers to improve conservation management strategies. We take advantage of recent developments in next-generation sequencing and network-based clustering to investigate conservation efforts for the native European Dark honey bee, A. m. mellifera, which is threatened by introgression in most of its range. We collected whole-genome sequence information from haploid drones of A. m. mellifera, A. m. carnica, and Buckfast sampled throughout Switzerland (N = 81), as well as from four Swiss A. m. mellifera conservation areas (N = 39) and from one conservatory in the French Alps (N = 31). Population structure analyses based upon 3.375 M genome-wide SNPs discerned samples by subspecies and geographic origin (Switzerland or France). Ancestry inference indicated admixed individuals in all of the protected areas, calling for improved management efforts. After testing different subsets of ancestry informative SNPs using three different selection strategies (F ST , PCA-based or at random), as few as 50 SNPs are found to be sufficient to differentiate native from introduced honey bees. Therefore, our data suggests that a low-density SNP panel can be a precise and cost-effective tool to support conservation management efforts for managed pollinators.

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“…To this end, we used a modified version of a two-way clustering approach [4]–[6]. In the first step, the algorithm processed all mRNA signals within one organ to define a number of tissue clusters.…”

Section: Introductionmentioning

confidence: 99%

Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study

et al. 2009

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Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n = 66/tissue) and atherosclerotic and unaffected arterial wall (n = 40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n = 15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n = 3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n = 49/48) and one visceral fat (n = 59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P = 0.008 and P = 0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n = 55/54) relating to carotid stenosis (P = 0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n = 16/17, P<10−27and−30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, by cellular and lesion expression of LDB2, and by the expression of 13 TEML genes in Ldb2–deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and, together with LDB2, merits further attention in CAD research.

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Super paramagnetic clustering of protein sequences

Cited by 35 publications

References 32 publications

Nonparametric approaches for population structure analysis

Nonparametric approaches for population structure analysis

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study

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