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[{"categories":["Python Package"],"contents":"EpiScanpy – Epigenomics single cell analysis in python EpiScanpy is a toolkit to analyse single-cell open chromatin (scATAC-seq) and single-cell DNA methylation (for example scBS-seq) data. EpiScanpy is the epigenomic extension of the very popular scRNA-seq analysis tool Scanpy Genome Biology, 2018. For more information, read scanpy documentation.\nTo install EpiScanpy, check Gibhub For document manual, check ReadTheDocs.io For tutorial and live demo, check CodeOcean For preprint of the tool, check BioRXiv For example data used in the paper, check Zenodo ","permalink":"https://www.biostatgen.org/post/2020-11-30-python-package-episcanpy/","tags":["EpiScanpy"],"title":"Python Package EpiScanpy"},{"categories":["Project"],"contents":"The investigation of population structure permits 1) relegating people to distinct ethnic gatherings living together at a specific locale, 2) examining movements from the cause of admixed populaces, and 3) evaluating and portraying bewildering of shared hereditary parentage in affiliation contemplates. Movement from one population into another locale will prompt admixture between the tribal gatherings leading to another admixed relative populace. African inhabitants are the most seasoned population in advancing current human species, given its single source in the African mainland between 200,000 years back and 350,000 years earlier, proofed by archeologists. African dialects have been ordered into different fundamental semantic families. Plant-growth-specialist populaces widely speak Niger-Kordofanian across Africa\u0026rsquo;s wide geographic conveyance. These heaps of conditions, atmospheres, diet, ways of life, and irresistible infection openness add to reliable particular pressing factors upon the African inhabitants, whose genome-wide genetic profiles have huge potential in uncovering principle parts of human population history and inherited vulnerability to sicknesses. The investigation of African and African American inherited diversity was overviewed with 1,327 microsatellites from the expansive mainland population categorized by self-depicted ethnic and phonetic gatherings. This homogeneity was checked in the Western African inhabitants of the Sahel Belt when utilizing ADMIXTURE. These inhabitants presented changing extents of just two bunches. One was more successive in Atlantic Western inhabitants (e.g., 90% in Mandenka). The other one was more continuous in Western/Central inhabitants, particularly in Esan and Yoruba of Nigeria (arriving at 74–81% recurrence). The Bantu relocation further greatly upset the first African family southerly of its place of source and somewhat influenced the more southern Sahelian populaces.\nKnown population analysis tools, fineSTRUCTURE and CHROMOPAINTER, have been applied to the African setting and tackled Western African bunching to a fine-scale size. The evidence indicated that most of sub-Saharan inhabitants shared a specific degree of inheritance from outside their present geographic area (dividing among various linguistic gatherings, for instance, western Bantu speakers having some contribution from western Pygmies). Further analysis, IPCAPS have been applied to Western African samples to build up a proof-of-idea for the analytic tool for fine-scale population structure. IPCAPS revealed that Western African inhabitants were in line with ADMIXTURE and fineSTRUCTURE results. Fine-scale population structure has been found in the Western African inhabitants utilizing IPCAPS as a hereditary grouping tool. The quantity of IPCAPS-assigned groups was generously more modest than the genuine number of African ethnic gatherings taken as info. Some IPCAPS-assigned groups were comprised of mixed African subpopulations. Nonetheless, three important fine-scale population structures were featured in the African inhabitants living in Cameroon, Gambia, Mali, Southwest USA, and Barbados.\nRead the technical details at DOI: 10.1007/s00439-019-02069-7\nPreview figure from the article\n","permalink":"https://www.biostatgen.org/post/2020-08-15-western-african/","tags":["IPCAPS"],"title":"Western African inhabitants"},{"categories":["Project"],"contents":"Biologically, a population is a group of living organisms of the same species that are living together and have the capability of interbreeding. Evolutionary processes such as migration, natural selection etc., has overtime contributed to the variations in the gene makeup of individuals that makes up a particular population. Subpopulations are as a result of these variations that are present in genes of individuals of a population. Population studies have several importance among which are; Information gotten from population studies can be used to infer population history, also studying the population of a place can reveal predisposing factors of disease in that place and several others. Population stratification is one of several population studies that is being carried out. Population stratification is the difference in allele frequencies between subpopulations in a population, possibly due to different ancestry, especially in the context of association studies. Analysis of population stratification must meet 4 main challenges namely:\nDetecting the structure of a population Assigning individuals to subpopulations Determining the number of optimal or primal subpopulations( i.e the dominant subpopulations) Determining the proportion of ancestral subpopulation. Overtime there has been an immense increase in the numbers and types of genotypes that exist among populations and this increase has resulted in difficulties in carrying out certain populations studies such as correctly estimating the subpopulations and assigning individuals to them, the increase has also resulted in difficulties in carrying out population structure analysis. Principal components analysis (PCA) is the common computational method used in carrying out population structure analysis. It can accurately detect population structure but has limitations in its accuracy in resolving subpopulations and assigning individuals to them due to the increasing numbers of genotype as time progresses with high-throughput genotyping technology. Therefore there is need for a more efficient computational method which despite the constant addition of genotypes to the population would accurately resolve subpopulations and assign individuals to them.\nA population structure analysis algorithm has been developed which does not have the limitations present in the principal component analysis (PCA). This more efficient algorithm is called iterative pruning PCA (ipPCA). It is capable of resolving subpopulations and assigning individuals to them.\nThis new computational method is highly efficient and is not limited by the constant influx of genotypes into the population. Therefore when carrying out population structure analysis, it is advisable to use iterative pruning PCA (ipPCA) method since it does not have the limitations present in the common principal components analysis (PCA).\nRead the technical details at DOI: 10.1186/s13029-019-0072-6\nPreview figure from the article\n","permalink":"https://www.biostatgen.org/post/2019-04-17-capturing-fine-scale-structure/","tags":["IPCAPS"],"title":"Capturing fine-scale structure"},{"categories":["R package"],"contents":"A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) doi:10.1007/BF01441146. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.\nTo install and use, visit Github To use, see the reference manual at biostatgen.org, rddr.io and rdocumentation.org ","permalink":"https://www.biostatgen.org/post/2018-06-14-r-package-filest/","tags":null,"title":"R package FILEST"},{"categories":["R package"],"contents":"The R package IPCAPS is an unsupervised clustering algorithm based on iterative pruning to capture population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis (ipPCA) algorithm (Intarapanich et al., 2009; Limpiti et al., 2011). The IPCAPS involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and Clustering EM estimation as in Lebret et al. (2015). In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package KRIS.\nTo install and use, visit BIO3 To check the old repository, visit Github To use, see the reference manual at biostatgen.org, rddr.io and rdocumentation.org ","permalink":"https://www.biostatgen.org/post/2018-06-11-r-package-ipcaps/","tags":["IPCAPS"],"title":"R Package IPCAPS"},{"categories":["R package"],"contents":"Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.\nTo install and use, visit Github To use, see the reference manual at biostatgen.org, rddr.io and rdocumentation.org ","permalink":"https://www.biostatgen.org/post/2018-06-04-r-package-kris/","tags":["KRIS","IPCAPS"],"title":"R Package KRIS"},{"categories":["Project"],"contents":"From the very beginning of human existence one thing has always been constant and that is movement. Humans just like animals tend to move about a lot. They rarely stay in one particular region for so long. Its either they travel to other places to visit, for work purposes or to settle down. Migration is quite high in the human populace. Asia being the most populous continent on planet earth has a huge variation in ethnicity, language and gene make up of its population and the constant movement of people in and out of that region has made it complicated to trace the origin of the diversity in that region.\nThailand is a region in Asia and is at the center of Mainland Southeast Asia (MSEA). It occupies a strategic position being that it is at the crossroad of ancient migration path between North and East Asia and Island Southeast Asia therefore would have people carrying the genes of ancestral migrants, yet extensive research on the genetics of the population has not been done on this region. Genetic substructure may exist within the Thai population since given its geographical location it must have been flooded with migrants from different parts of Asia particularly Southern Asia and so would have substantial gene flow. The earliest archeological evidence of humans in MSEA was obtained in southern Thailand and DNA analysis of this specimen showed close relationship with the present day Semang population in Peninsula Malaysia. Studies have also shown that it’s likely that the first population of significance in the MSEA were established by Austric agriculturist people, the ancestors of Austroasiatic and Austronesians who may have originated in Southern China. The Tai people migrated from Southern China into Northern Thailand more recently and they have established settlements in Thailand alongside the indigenous Austric and eventually the Tai became dominant and established control over Northern Thailand.\nLarge scale study of the genetic variation of the population of Thailand has not been carried out. So to better our understanding of the Mainland Southeast Asia ( MSEA) and the Thai population genetics, a study was carried out and result of that study showed that the Thai population is genetically distinct from other Asian population, but there is evidence of shared ancestry supporting the known origin and historical migration patterns across MSEA. The study also clarified the Thai population structure, revealing four major subpopulations. A major ancestry is common among these four subpopulations which probably are the evidence of Austric ancestors who originally settled across most of MSEA.\nN.B: Tai refers to the group that have migrated from Southern China while Thai refers to the people presently living in Thailand.\nRead the technical details at DOI: 10.1371/journal.pone.0079522\nPreview figure from the article\n","permalink":"https://www.biostatgen.org/post/2013-12-18-sub-populations-of-thailand/","tags":["IPCAPS"],"title":"Sub-populations of Thailand"},{"categories":null,"contents":"Dr Chaichoompu has scientific expertise in informatics (parallel/high-performance computing and machine learning), bioinformatics, and biostatistics. He has been working for multidisciplinary scientific research since he started his research career in 2006 as a Research Assistant at Biostatistics and Informatics Laboratory, Genome Institute, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand. During his time at BIOTEC, he was involved in many research projects which could be categorized into five main areas: genomic data analysis, population structure analysis, tool and algorithm development, web application and database development, and computer hardware acceleration.\nIn 2017, he obtained his PhD in Applied Science (Bioinformatics) from Université de Liège, Belgium. His PhD research was involved in methodology development to detect fine-scale population structure towards patient molecular reclassification. During his PhD study, he also gained experience in teaching and organizing conferences. Besides, he has been working with a molecular subgrouping working group and an epistasis working group of the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) since the early period of his PhD study.\nLater, he became a post-doctoral researcher at Max Planck Institute of Psychiatry in Munich, Germany, until December 2018. His postdoctoral research was about methodology development for genetic analysis in patients using machine learning and deep learning. Currently, he is working as a post-doctoral researcher at Helmholtz Zentrum München since May 2019.\n","permalink":"https://www.biostatgen.org/author/kridsadakorn-chaichoompu/","tags":null,"title":"Kridsadakorn Chaichoompu"}]