HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph.
Ontology highlight
ABSTRACT:
SUBMITTER: Rubin JD
PROVIDER: S-EPMC10281577 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
Publications
HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph.
Rubin Joshua Daniel JD Vogel Nicola Alexandra NA Gopalakrishnan Shyam S Sackett Peter Wad PW Renaud Gabriel G
PLoS computational biology 20230607 6
Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that ou ...[more]
Similar Datasets
Project description:Many studies have reported the frequency and distribution of haplogroups among various cattle breeds for verification of their origins and genetic diversity. In this study, 318 complete sequences of the mtDNA control region from four Korean cattle breeds were used for haplogroup classification. 71 polymorphic sites and 66 haplotypes were found in these sequences. Consistent with the genetic patterns in previous reports, four haplogroups (T1, T2, T3, and T4) were identified in Korean cattle breeds. In addition, T1a, T3a, and T3b sub-haplogroups were classified. In the phylogenetic tree, each haplogroup formed an independent cluster. The frequencies of T3, T4, T1 (containing T1a), and T2 were 66%, 16%, 10%, and 8%, respectively. Especially, the T1 haplogroup contained only one haplotype and a sample. All four haplogroups were found in Chikso, Jeju black and Hanwoo. However, only the T3 and T4 haplogroups appeared in Heugu, and most Chikso populations showed a partial of four haplogroups. These results will be useful for stable conservation and efficient management of Korean cattle breeds.
| S-EPMC4852222 | biostudies-literature
Project description:BACKGROUND:Although our microbial community and genomes (the human microbiome) outnumber our genome by several orders of magnitude, to what extent the human host genetic complement informs the microbiota composition is not clear. The Human Microbiome Project (HMP) Consortium established a unique population-scale framework with which to characterize the relationship of microbial community structure with their human hosts. A wide variety of taxa and metabolic pathways have been shown to be differentially distributed by virtue of race/ethnicity in the HMP. Given that mtDNA haplogroups are the maternally derived ancestral genomic markers and mitochondria's role as the generator for cellular ATP, characterizing the relationship between human mtDNA genomic variants and microbiome profiles becomes of potential marked biologic and clinical interest. RESULTS:We leveraged sequencing data from the HMP to investigate the association between microbiome community structures with its own host mtDNA variants. 15 haplogroups and 631 mtDNA nucleotide polymorphisms (mean sequencing depth of 280X on the mitochondria genome) from 89 individuals participating in the HMP were accurately identified. 16S rRNA (V3-V5 region) sequencing generated microbiome taxonomy profiles and whole genome shotgun sequencing generated metabolic profiles from various body sites were treated as traits to conduct association analysis between haplogroups and host clinical metadata through linear regression. The mtSNPs of individuals with European haplogroups were associated with microbiome profiles using PLINK quantitative trait associations with permutation and adjusted for multiple comparisons. We observe that among 139 stool and 59 vaginal posterior fornix samples, several haplogroups show significant association with specific microbiota (q-value < 0.05) as well as their aggregate community structure (Chi-square with Monte Carlo, p < 0.005), which confirmed and expanded previous research on the association of race and ethnicity with microbiome profile. Our results further indicate that mtDNA variations may render different microbiome profiles, possibly through an inflammatory response to different levels of reactive oxygen species activity. CONCLUSIONS:These data provide initial evidence for the association between host ancestral genome with the structure of its microbiome.
| S-EPMC4234434 | biostudies-literature
Project description:Mitochondrial DNA haplogroup classification is used to study maternal lineage of ancient human populations. The haplogrouping of ancient DNA is not easy because the DNA is usually found in small pieces in limited quantities. We have developed Haplotracker, a straightforward and efficient high-resolution haplogroup classification tool optimized specifically for ancient DNA samples. Haplotracker offers a user-friendly input interface for multiple mitochondrial DNA sequence fragments in a sample. It provides accurate haplogroup classification with full-length mitochondrial genome sequences and provides high-resolution haplogroup predictions for some fragmented control region sequences using a novel algorithm built on Phylotree mtDNA Build 17 (Phylotree) and our haplotype database (n = 118,869). Its performance for accuracy was demonstrated to be high through haplogroup classification using 8,216 Phylotree full-length and control region mitochondrial DNA sequences compared with HaploGrep 2, one of the most accurate current haplogroup classifiers. Haplotracker provides a novel haplogroup tracking solution for fragmented sequences to track subhaplogroups or verify the haplogroups efficiently. Using Haplotracker, we classified mitochondrial haplogroups to the final subhaplogroup level in nine ancient DNA samples extracted from human skeletal remains found in 2,000-year-old elite Xiongnu cemetery in Northeast Mongolia. Haplotracker can be freely accessed at https://haplotracker.cau.ac.kr.
| S-EPMC8956381 | biostudies-literature
Project description: Not available
| S-EPMC2841866 | biostudies-literature
Project description:BACKGROUND: Human genetic diversity observed in Indian subcontinent is second only to that of Africa. This implies an early settlement and demographic growth soon after the first 'Out-of-Africa' dispersal of anatomically modern humans in Late Pleistocene. In contrast to this perspective, linguistic diversity in India has been thought to derive from more recent population movements and episodes of contact. With the exception of Dravidian, which origin and relatedness to other language phyla is obscure, all the language families in India can be linked to language families spoken in different regions of Eurasia. Mitochondrial DNA and Y chromosome evidence has supported largely local evolution of the genetic lineages of the majority of Dravidian and Indo-European speaking populations, but there is no consensus yet on the question of whether the Munda (Austro-Asiatic) speaking populations originated in India or derive from a relatively recent migration from further East. RESULTS: Here, we report the analysis of 35 novel complete mtDNA sequences from India which refine the structure of Indian-specific varieties of haplogroup R. Detailed analysis of haplogroup R7, coupled with a survey of approximately 12,000 mtDNAs from caste and tribal groups over the entire Indian subcontinent, reveals that one of its more recently derived branches (R7a1), is particularly frequent among Munda-speaking tribal groups. This branch is nested within diverse R7 lineages found among Dravidian and Indo-European speakers of India. We have inferred from this that a subset of Munda-speaking groups have acquired R7 relatively recently. Furthermore, we find that the distribution of R7a1 within the Munda-speakers is largely restricted to one of the sub-branches (Kherwari) of northern Munda languages. This evidence does not support the hypothesis that the Austro-Asiatic speakers are the primary source of the R7 variation. Statistical analyses suggest a significant correlation between genetic variation and geography, rather than between genes and languages. CONCLUSION: Our high-resolution phylogeographic study, involving diverse linguistic groups in India, suggests that the high frequency of mtDNA haplogroup R7 among Munda speaking populations of India can be explained best by gene flow from linguistically different populations of Indian subcontinent. The conclusion is based on the observation that among Indo-Europeans, and particularly in Dravidians, the haplogroup is, despite its lower frequency, phylogenetically more divergent, while among the Munda speakers only one sub-clade of R7, i.e. R7a1, can be observed. It is noteworthy that though R7 is autochthonous to India, and arises from the root of hg R, its distribution and phylogeography in India is not uniform. This suggests the more ancient establishment of an autochthonous matrilineal genetic structure, and that isolation in the Pleistocene, lineage loss through drift, and endogamy of prehistoric and historic groups have greatly inhibited genetic homogenization and geographical uniformity.
| S-EPMC2529308 | biostudies-literature
Project description:The somatic recombination of V, D, and J gene segments in B-cells introduces a great deal of diversity, and divergence from reference segments. Many recent studies of antibodies focus on the population of antibody transcripts that show which V, D, and J gene segments have been favored for a particular antigen, a repertoire. To properly describe the antibody repertoire, each antibody must be labeled by its constituting V, D, and J gene segment, a task made difficult by somatic recombination and hypermutation events. While previous approaches to repertoire analysis were based on sequential alignments, we describe a new de Bruijn graph-based algorithm to perform VDJ labeling and benchmark its performance.
| S-EPMC4904161 | biostudies-literature
Project description:BackgroundAnalyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures.ResultsIn this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods.ConclusionsResults indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.
| S-EPMC8268184 | biostudies-literature
Project description:Mitochondrial dysfunction is associated with several retinal degenerative diseases including Age-related Macular Degeneration (AMD). Human mitochondrial DNA (mtDNA) haplogroups are inherited from a common ancestral clan and are defined by specific sets of genetic differences. The purpose of this study was to determine and compare the effects of mtDNA haplogroups H and J on transcriptome regulation and cellular resilience to oxidative stress in human RPE cytoplasmic hybrid (cybrid) cell lines in vitro. ARPE-19 cybrid cell lines containing mtDNA haplogroups H and J were created by fusing platelets obtained from normal individuals containing H and J haplogroups with mitochondria-deficient (Rho0) ARPE-19 cell lines. These cybrids were exposed to oxidative stress using 300 μM hydrogen peroxide (H2O2), following which mitochondrial structural dynamics was studied at varying time points using the mitochondrial markers - TOMM20 (Translocase of Outer Mitochondrial Membrane 20) and Mitotracker. To evaluate mitochondrial function, levels of ROS, ΔΨm and [Ca2+]m were measured using flow cytometry, and ATP levels were measured using luminescence. The H and J cybrid cell transcriptomes were compared using RNAseq to determine how changes in mtDNA regulate gene expression. Inflammatory and angiogenic markers were measured using Luminex assay to understand how these mtDNAs influenced cellular response to oxidative stress. Actin filaments' morphology was examined using confocal microscopy. Following exposure to H2O2 stress, the J cybrids showed increased mitochondrial swelling and perinuclear localization, disturbed fission and fusion, increased calcium uptake (p < 0.05), and higher secreted levels of TNF-α and VEGF (p < 0.001), compared to the H cybrids. Calcium uptake by J cybrids was reduced using an IP3R inhibitor. Thirteen genes involved in mitochondrial complex I and V function, fusion/fission events, cellular energy homeostasis, antioxidant defenses, and inflammatory responses, were significantly downregulated with log2 fold changes ranging between -1.5 and -5.1. Actin levels were also significantly reduced in stressed J cybrids (p ≤ 0.001) and disruption in actin filaments was observed. Thirty-eight genes involved in mitochondrial and cellular support functions, were upregulated with log2 fold changes of +1.5 to +5.9 in J cybrids compared to H cybrids. Our results demonstrate significant structural and functional differences between mtDNA haplogroups H vs. J -containing cybrid cells. Our study suggests that the J mtDNA haplogroup can alter the transcriptome to increase cellular susceptibility to stress and retinal degenerations.
| S-EPMC9949352 | biostudies-literature
Project description:BackgroundCancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.ResultsIn this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific.ConclusionNovel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.
| S-EPMC7799442 | biostudies-literature
Project description:MotivationBacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data.ResultsIn this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools.Availability and implementationThe source code of PhaGCN is available via: https://github.com/KennthShang/PhaGCN.
| S-EPMC8275337 | biostudies-literature