Iterative pruning PCA improves resolution of highly structured populations

BACKGROUND: Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming. RESULTS: A novel population structure analysis algorithm called iterative pruning PCA (ipPCA) was developed which assigns individuals to subpopulations and infers the total number of subpopulations present. Genotypic data from simulated and real population datasets with different degrees of structure were analyzed. For datasets with simple structures, the subpopulation assignments of individuals made by ipPCA were largely consistent with the STRUCTURE, BAPS and AWclust algorithms. On the other hand, highly structured populations containing many closely related subpopulations could be accurately resolved only by ipPCA, and not by other methods. CONCLUSION: The algorithm is computationally efficient and not constrained by the dataset complexity. This systematic subpopulation assignment approach removes the need for prior population labels, which could be advantageous when cryptic stratification is encountered in datasets containing individuals otherwise assumed to belong to a homogenous population

Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure

<p>Abstract</p> <p>Background</p> <p>The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.</p> <p>Results</p> <p>A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.</p> <p>Conclusions</p> <p>The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from <url>http://www4a.biotec.or.th/GI/tools/ippca</url>.</p

Plasmodium parasites mount an arrest response to dihydroartemisinin, as revealed by whole transcriptome shotgun sequencing (RNA-seq) and microarray study

RNA-seq data analysis from DHA treatment of P. falciparum Limma results from 1 h treatments with 500 nM DHA in P. falciparum K1 rings, trophozoites and schizonts. (XLS 2040 kb

Comparison of gene expression profiles between human erythroid cells derived from fetal liver and adult peripheral blood

Background A key event in human development is the establishment of erythropoietic progenitors in the bone marrow, which is accompanied by a fetal-to-adult switch in hemoglobin expression. Understanding of this event could lead to medical application, notably treatment of sickle cell disease and β-thalassemia. The changes in gene expression of erythropoietic progenitor cells as they migrate from the fetal liver and colonize the bone marrow are still rather poorly understood, as primary fetal liver (FL) tissues are difficult to obtain. Methods We obtained human FL tissue and adult peripheral blood (AB) samples from Thai subjects. Primary CD34+ cells were cultured in vitro in a fetal bovine serum-based culture medium. After 8 days of culture, erythroid cell populations were isolated by flow cytometry. Gene expression in the FL- and AB-derived cells was studied by Affymetrix microarray and reverse-transcription quantitative PCR. The microarray data were combined with that from a previous study of human FL and AB erythroid development, and meta-analysis was performed on the combined dataset. Results FL erythroid cells showed enhanced proliferation and elevated fetal hemoglobin relative to AB cells. A total of 1,391 fetal up-regulated and 329 adult up-regulated genes were identified from microarray data generated in this study. Five hundred ninety-nine fetal up-regulated and 284 adult up-regulated genes with reproducible patterns between this and a previous study were identified by meta-analysis of the combined dataset, which constitute a core set of genes differentially expressed between FL and AB erythroid cells. In addition to these core genes, 826 and 48 novel genes were identified only from data generated in this study to be FL up- and AB up-regulated, respectively. The in vivo relevance for some of these novel genes was demonstrated by pathway analysis, which showed novel genes functioning in pathways known to be important in proliferation and erythropoiesis, including the mitogen-activated protein kinase (MAPK) and the phosphatidyl inositol 3 kinase (PI3K)-Akt pathways. Discussion The genes with upregulated expression in FL cells, which include many novel genes identified from data generated in this study, suggest that cellular proliferation pathways are more active in the fetal stage. Erythroid progenitor cells may thus undergo a reprogramming during ontogenesis in which proliferation is modulated by changes in expression of key regulators, primarily MYC, and others including insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), neuropilin and tolloid-like 2 (NETO2), branched chain amino acid transaminase 1 (BCAT1), tenascin XB (TNXB) and proto-oncogene, AP-1 transcription factor subunit (JUND). This reprogramming may thus be necessary for acquisition of the adult identity and switching of hemoglobin expression

Uncovering full-length transcript isoforms of sugarcane cultivar Khon Kaen 3 using single-molecule long-read sequencing

Background Sugarcane is an important global food crop and energy resource. To facilitate the sugarcane improvement program, genome and gene information are important for studying traits at the molecular level. Most currently available transcriptome data for sugarcane were generated using second-generation sequencing platforms, which provide short reads. The de novo assembled transcripts from these data are limited in length, and hence may be incomplete and inaccurate, especially for long RNAs. Methods We generated a transcriptome dataset of leaf tissue from a commercial Thai sugarcane cultivar Khon Kaen 3 (KK3) using PacBio RS II single-molecule long-read sequencing by the Iso-Seq method. Short-read RNA-Seq data were generated from the same RNA sample using the Ion Proton platform for reducing base calling errors. Results A total of 119,339 error-corrected transcripts were generated with the N50 length of 3,611 bp, which is on average longer than any previously reported sugarcane transcriptome dataset. 110,253 sequences (92.4%) contain an open reading frame (ORF) of at least 300 bp long with ORF N50 of 1,416 bp. The mean lengths of 5′ and 3′ untranslated regions in 73,795 sequences with complete ORFs are 1,249 and 1,187 bp, respectively. 4,774 transcripts are putatively novel full-length transcripts which do not match with a previous Iso-Seq study of sugarcane. We annotated the functions of 68,962 putative full-length transcripts with at least 90% coverage when compared with homologous protein coding sequences in other plants. Discussion The new catalog of transcripts will be useful for genome annotation, identification of splicing variants, SNP identification, and other research pertaining to the sugarcane improvement program. The putatively novel transcripts suggest unique features of KK3, although more data from different tissues and stages of development are needed to establish a reference transcriptome of this cultivar

microPIR: An Integrated Database of MicroRNA Target Sites within Human Promoter Sequences

Background: microRNAs are generally understood to regulate gene expression through binding to target sequences within 39-UTRs of mRNAs. Therefore, computational prediction of target sites is usually restricted to these gene regions. Recent experimental studies though have suggested that microRNAs may alternatively modulate gene expression by interacting with promoters. A database of potential microRNA target sites in promoters would stimulate research in this field leading to more understanding of complex microRNA regulatory mechanism. Methodology: We developed a database hosting predicted microRNA target sites located within human promoter sequences and their associated genomic features, called microPIR (microRNA-Promoter Interaction Resource). microRNA seed sequences were used to identify perfect complementary matching sequences in the human promoters and the potential target sites were predicted using the RNAhybrid program..15 million target sites were identified which are located within 5000 bp upstream of all human genes, on both sense and antisense strands. The experimentally confirmed argonaute (AGO) binding sites and EST expression data including the sequence conservation across vertebrate species of each predicted target are presented for researchers to appraise the quality of predicted target sites. The microPIR database integrates various annotated genomic sequence databases, e.g. repetitive elements, transcription factor binding sites, CpG islands, and SNPs, offering users the facility to extensively explore relationships among target sites and other genomi

Hypomethylation of Intragenic LINE-1 Represses Transcription in Cancer Cells through AGO2

In human cancers, the methylation of long interspersed nuclear element -1 (LINE-1 or L1) retrotransposons is reduced. This occurs within the context of genome wide hypomethylation, and although it is common, its role is poorly understood. L1s are widely distributed both inside and outside of genes, intragenic and intergenic, respectively. Interestingly, the insertion of active full-length L1 sequences into host gene introns disrupts gene expression. Here, we evaluated if intragenic L1 hypomethylation influences their host gene expression in cancer. First, we extracted data from L1base (http://l1base.molgen.mpg.de), a database containing putatively active L1 insertions, and compared intragenic and intergenic L1 characters. We found that intragenic L1 sequences have been conserved across evolutionary time with respect to transcriptional activity and CpG dinucleotide sites for mammalian DNA methylation. Then, we compared regulated mRNA levels of cells from two different experiments available from Gene Expression Omnibus (GEO), a database repository of high throughput gene expression data, (http://www.ncbi.nlm.nih.gov/geo) by chi-square. The odds ratio of down-regulated genes between demethylated normal bronchial epithelium and lung cancer was high (p<1E−27; OR = 3.14; 95% CI = 2.54–3.88), suggesting cancer genome wide hypomethylation down-regulating gene expression. Comprehensive analysis between L1 locations and gene expression showed that expression of genes containing L1s had a significantly higher likelihood to be repressed in cancer and hypomethylated normal cells. In contrast, many mRNAs derived from genes containing L1s are elevated in Argonaute 2 (AGO2 or EIF2C2)-depleted cells. Hypomethylated L1s increase L1 mRNA levels. Finally, we found that AGO2 targets intronic L1 pre-mRNA complexes and represses cancer genes. These findings represent one of the mechanisms of cancer genome wide hypomethylation altering gene expression. Hypomethylated intragenic L1s are a nuclear siRNA mediated cis-regulatory element that can repress genes. This epigenetic regulation of retrotransposons likely influences many aspects of genomic biology