More data on ancient human mitogenome variability in Italy: new mitochondrial genome sequences from three Upper Palaeolithic burials.

BACKGROUND: Recently, the study of mitochondrial variability in ancient humans has allowed the definition of population dynamics that characterised Europe in the Late Pleistocene and Early Holocene. Despite the abundance of sites and skeletal remains few data are available for Italy. AIM: We reconstructed the mitochondrial genomes of three Upper Palaeolithic individuals for some of the most important Italian archaeological contexts: Paglicci (South-Eastern Italy), San Teodoro (South-Western Italy) and Arene Candide (North-Western Italy) caves. SUBJECTS AND METHODS: We explored the phylogenetic relationships of the three mitogenomes in the context of Western Eurasian ancient and modern variability. RESULTS: Paglicci 12 belongs to sub-haplogroup U8c, described in only two other Gravettian individuals; San Teodoro 2 harbours a U2'3'4'7'8'9 sequence, the only lineage found in Sicily during the Late Pleistocene and Early Holocene; Arene Candide 16 displays an ancestral U5b1 haplotype already detected in other Late Pleistocene hunter-gatherers from Central Europe. CONCLUSION: Regional genetic continuity is highlighted in the Gravettian groups that succeeded in Paglicci. Data from one of the oldest human remains from Sicily reinforce the hypothesis that Epigravettian groups carrying U2'3'4'7'8'9 could be the first inhabitants of the island. The first pre-Neolithic mitogenome from North-Western Italy, sequenced here, shows more affinity with continental Europe than with the Italian peninsula.

How a Paleogenomic Approach Can Provide Details on Bioarchaeological Reconstruction: A Case Study from the Globular Amphorae Culture.

Ancient human remains have the potential to explain a great deal about the prehistory of humankind. Due to recent technological and bioinformatics advances, their study, at the palaeogenomic level, can provide important information about population dynamics, culture changes, and the lifestyles of our ancestors. In this study, mitochondrial and nuclear genome data obtained from human bone remains associated with the Neolithic Globular Amphorae culture, which were recovered in the Megalithic barrow of Kierzkowo (Poland), were reanalysed to gain insight into the social organisation and use of the archaeological site and to provide information at the individual level. We were able to successfully estimate the minimum number of individuals, sex, kin relationships, and phenotypic traits of the buried individuals, despite the low level of preservation of the bone samples and the intricate taphonomic conditions. In addition, the evaluation of damage patterns allowed us to highlight the presence of "intruders"-that is, of more recent skeletal remains that did not belong to the original burial. Due to its characteristics, the study of the Kierzkowo barrow represented a challenge for the reconstruction of the biological profile of the human community who exploited it and an excellent example of the contribution that ancient genomic analysis can provide to archaeological reconstruction.

New Insights Into Mitochondrial DNA Reconstruction and Variant Detection in Ancient Samples.

Ancient DNA (aDNA) studies are frequently focused on the analysis of the mitochondrial DNA (mtDNA), which is much more abundant than the nuclear genome, hence can be better retrieved from ancient remains. However, postmortem DNA damage and contamination make the data analysis difficult because of DNA fragmentation and nucleotide alterations. In this regard, the assessment of the heteroplasmic fraction in ancient mtDNA has always been considered an unachievable goal due to the complexity in distinguishing true endogenous variants from artifacts. We implemented and applied a computational pipeline for mtDNA analysis to a dataset of 30 ancient human samples from an Iron Age necropolis in Polizzello (Sicily, Italy). The pipeline includes several modules from well-established tools for aDNA analysis and a recently released variant caller, which was specifically conceived for mtDNA, applied for the first time to aDNA data. Through a fine-tuned filtering on variant allele sequencing features, we were able to accurately reconstruct nearly complete (>88%) mtDNA genome for almost all the analyzed samples (27 out of 30), depending on the degree of preservation and the sequencing throughput, and to get a reliable set of variants allowing haplogroup prediction. Additionally, we provide guidelines to deal with possible artifact sources, including nuclear mitochondrial sequence (NumtS) contamination, an often-neglected issue in ancient mtDNA surveys. Potential heteroplasmy levels were also estimated, although most variants were likely homoplasmic, and validated by data simulations, proving that new sequencing technologies and software are sensitive enough to detect partially mutated sites in ancient genomes and discriminate true variants from artifacts. A thorough functional annotation of detected and filtered mtDNA variants was also performed for a comprehensive evaluation of these ancient samples.

Bioinformatics Resources for RNA Editing.

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or processing of noncoding RNAs (ncRNAs). In recent years, the detection of editing sites has been greatly improved through the availability of high-throughput RNA sequencing technologies. Several pipelines, employing various read mappers and variant callers with a wide range of adjustable parameters, are currently available for the detection of RNA editing events. Hereafter, we describe some of the most recent and popular tools and provide guidelines for the detection of RNA editing in massive transcriptome data.

HPC-REDItools: a novel HPC-aware tool for improved large scale RNA-editing analysis.

BACKGROUND: RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process. RESULTS: In this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. AVAILABILITY: https://github.com/BioinfoUNIBA/REDItools2 . CONCLUSIONS: HPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores.

Investigating Human Mitochondrial Genomes in Single Cells.

Mitochondria host multiple copies of their own small circular genome that has been extensively studied to trace the evolution of the modern eukaryotic cell and discover important mutations linked to inherited diseases. Whole genome and exome sequencing have enabled the study of mtDNA in a large number of samples and experimental conditions at single nucleotide resolution, allowing the deciphering of the relationship between inherited mutations and phenotypes and the identification of acquired mtDNA mutations in classical mitochondrial diseases as well as in chronic disorders, ageing and cancer. By applying an ad hoc computational pipeline based on our MToolBox software, we reconstructed mtDNA genomes in single cells using whole genome and exome sequencing data obtained by different amplification methodologies (eWGA, DOP-PCR, MALBAC, MDA) as well as data from single cell Assay for Transposase Accessible Chromatin with high-throughput sequencing (scATAC-seq) in which mtDNA sequences are expected as a byproduct of the technology. We show that assembled mtDNAs, with the exception of those reconstructed by MALBAC and DOP-PCR methods, are quite uniform and suitable for genomic investigations, enabling the study of various biological processes related to cellular heterogeneity such as tumor evolution, neural somatic mosaicism and embryonic development.

Elucidating the editome: bioinformatics approaches for RNA editing detection.

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or the processing of noncoding RNAs. In recent years, the detection of editing sites has been improved through the availability of high-throughput RNA sequencing (RNA-Seq) technologies. Accurate bioinformatics pipelines are essential for the analysis of next-generation sequencing (NGS) data to ensure the correct identification of edited sites. Several pipelines, using various read mappers and variant callers with a wide range of adjustable parameters, are available for the detection of RNA editing events. In this review, we discuss some of the most recent and popular tools and provide guidelines for RNA-Seq data generation and analysis for the detection of RNA editing in massive transcriptome data. Using simulated and real data sets, we provide an overview of their behavior, emphasizing the fact that the RNA editing detection in NGS data sets remains a challenging task.

A multi-gene panel beyond BRCA1/BRCA2 to identify new breast cancer-predisposing mutations by a picodroplet PCR followed by a next-generation sequencing strategy: a pilot study.

By analyzing multiple gene panels, next-generation sequencing is more effective than conventional procedures in identifying disease-related mutations that are useful for clinical decision-making. Here, we aimed to test the efficacy of an 84 genes customized-panel in BRCA1 and BRCA2 mutation-negative patients. Twenty-four patients were enrolled in this study. DNA libraries were prepared using a picodroplet PCR-based approach and sequenced with the MiSeq System. Highly putative pathogenic mutations were identified in genes other than the commonly tested BRCA1/2: 2 pathogenic mutations one in TP53 and one in MUTYH; 2 missense variants in MSH6 and ATM, respectively; 2 frameshift variants in KLLN, and ATAD2, respectively; an intronic variant in ANPEP, and 3 not functionally known variants (a frameshift variant in ATM a nonsense variant in ATM and a missense variant in NFE2L2). Our results show that this molecular screening will increase diagnostic sensitivity leading to a better risk assessment in breast cancer patients and their families. This strategy could also reveal genes that have a higher penetrance for breast and ovarian cancers by matching gene mutation with familial and clinical data, thereby increasing information about hereditary breast and ovarian cancer genetics and improving cancer prevention measures or therapeutic approaches.

HmtDB 2016: data update, a better performing query system and human mitochondrial DNA haplogroup predictor.

The HmtDB resource hosts a database of human mitochondrial genome sequences from individuals with healthy and disease phenotypes. The database is intended to support both population geneticists as well as clinicians undertaking the task to assess the pathogenicity of specific mtDNA mutations. The wide application of next-generation sequencing (NGS) has provided an enormous volume of high-resolution data at a low price, increasing the availability of human mitochondrial sequencing data, which called for a cogent and significant expansion of HmtDB data content that has more than tripled in the current release. We here describe additional novel features, including: (i) a complete, user-friendly restyling of the web interface, (ii) links to the command-line stand-alone and web versions of the MToolBox package, an up-to-date tool to reconstruct and analyze human mitochondrial DNA from NGS data and (iii) the implementation of the Reconstructed Sapiens Reference Sequence (RSRS) as mitochondrial reference sequence. The overall update renders HmtDB an even more handy and useful resource as it enables a more rapid data access, processing and analysis. HmtDB is accessible at http://www.hmtdb.uniba.it/.

MSeqDR: A Centralized Knowledge Repository and Bioinformatics Web Resource to Facilitate Genomic Investigations in Mitochondrial Disease.

MSeqDR is the Mitochondrial Disease Sequence Data Resource, a centralized and comprehensive genome and phenome bioinformatics resource built by the mitochondrial disease community to facilitate clinical diagnosis and research investigations of individual patient phenotypes, genomes, genes, and variants. A central Web portal (https://mseqdr.org) integrates community knowledge from expert-curated databases with genomic and phenotype data shared by clinicians and researchers. MSeqDR also functions as a centralized application server for Web-based tools to analyze data across both mitochondrial and nuclear DNA, including investigator-driven whole exome or genome dataset analyses through MSeqDR-Genesis. MSeqDR-GBrowse genome browser supports interactive genomic data exploration and visualization with custom tracks relevant to mtDNA variation and mitochondrial disease. MSeqDR-LSDB is a locus-specific database that currently manages 178 mitochondrial diseases, 1,363 genes associated with mitochondrial biology or disease, and 3,711 pathogenic variants in those genes. MSeqDR Disease Portal allows hierarchical tree-style disease exploration to evaluate their unique descriptions, phenotypes, and causative variants. Automated genomic data submission tools are provided that capture ClinVar compliant variant annotations. PhenoTips will be used for phenotypic data submission on deidentified patients using human phenotype ontology terminology. The development of a dynamic informed patient consent process to guide data access is underway to realize the full potential of these resources.

A comprehensive collection of annotations to interpret sequence variation in human mitochondrial transfer RNAs.

BACKGROUND: The abundance of biological data characterizing the genomics era is contributing to a comprehensive understanding of human mitochondrial genetics. Nevertheless, many aspects are still unclear, specifically about the variability of the 22 human mitochondrial transfer RNA (tRNA) genes and their involvement in diseases. The complex enrichment and isolation of tRNAs in vitro leads to an incomplete knowledge of their post-transcriptional modifications and three-dimensional folding, essential for correct tRNA functioning. An accurate annotation of mitochondrial tRNA variants would be definitely useful and appreciated by mitochondrial researchers and clinicians since the most of bioinformatics tools for variant annotation and prioritization available so far cannot shed light on the functional role of tRNA variations. RESULTS: To this aim, we updated our MToolBox pipeline for mitochondrial DNA analysis of high throughput and Sanger sequencing data by integrating tRNA variant annotations in order to identify and characterize relevant variants not only in protein coding regions, but also in tRNA genes. The annotation step in the pipeline now provides detailed information for variants mapping onto the 22 mitochondrial tRNAs. For each mt-tRNA position along the entire genome, the relative tRNA numbering, tRNA type, cloverleaf secondary domains (loops and stems), mature nucleotide and interactions in the three-dimensional folding were reported. Moreover, pathogenicity predictions for tRNA and rRNA variants were retrieved from the literature and integrated within the annotations provided by MToolBox, both in the stand-alone version and web-based tool at the Mitochondrial Disease Sequence Data Resource (MSeqDR) website. All the information available in the annotation step of MToolBox were exploited to generate custom tracks which can be displayed in the GBrowse instance at MSeqDR website. CONCLUSIONS: To the best of our knowledge, specific data regarding mitochondrial variants in tRNA genes were introduced for the first time in a tool for mitochondrial genome analysis, supporting the interpretation of genetic variants in specific genomic contexts.

A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest.

Assigning a pathogenic role to mitochondrial DNA (mtDNA) variants and unveiling the potential involvement of the mitochondrial genome in diseases are challenging tasks in human medicine. Assuming that rare variants are more likely to be damaging, we designed a phylogeny-based prioritization workflow to obtain a reliable pool of candidate variants for further investigations. The prioritization workflow relies on an exhaustive functional annotation through the mtDNA extraction pipeline MToolBox and includes Macro Haplogroup Consensus Sequences to filter out fixed evolutionary variants and report rare or private variants, the nucleotide variability as reported in HmtDB and the disease score based on several predictors of pathogenicity for non-synonymous variants. Cutoffs for both the disease score as well as for the nucleotide variability index were established with the aim to discriminate sequence variants contributing to defective phenotypes. The workflow was validated on mitochondrial sequences from Leber's Hereditary Optic Neuropathy affected individuals, successfully identifying 23 variants including the majority of the known causative ones. The application of the prioritization workflow to cancer datasets allowed to trim down the number of candidate for subsequent functional analyses, unveiling among these a high percentage of somatic variants. Prioritization criteria were implemented in both standalone ( http://sourceforge.net/projects/mtoolbox/ ) and web version ( https://mseqdr.org/mtoolbox.php ) of MToolBox.

Mitochondrial Disease Sequence Data Resource (MSeqDR): a global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities.

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The "Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium" is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through the use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial diseases.

Extraction and annotation of human mitochondrial genomes from 1000 Genomes Whole Exome Sequencing data.

BACKGROUND: Whole Exome Sequencing (WES) is one of the most used and cost-effective next generation technologies that allows sequencing of all nuclear exons. Off-target regions may be captured if they present high sequence similarity with baits. Bioinformatics tools have been optimized to retrieve a large amount of WES off-target mitochondrial DNA (mtDNA), by exploiting the aspecificity of probes, partially overlapping to Nuclear mitochondrial Sequences (NumtS). The 1000 Genomes project represents one of the widest resources to extract mtDNA sequences from WES data, considering the large effort the scientific community is undertaking to reconstruct human population history using mtDNA as marker, and the involvement of mtDNA in pathology. RESULTS: A previously published pipeline aimed at assembling mitochondrial genomes from off-target WES reads and further improved to detect insertions and deletions (indels) and heteroplasmy in a dataset of 1242 samples from the 1000 Genomes project, enabled to obtain a nearly complete mitochondrial genome from 943 samples (76% analyzed exomes). The robustness of our computational strategy was highlighted by the reduction of reads amount recognized as mitochondrial in the original annotation produced by the Consortium, due to NumtS filtering. CONCLUSIONS: To the best of our knowledge, this is likely the most extended population-scale mitochondrial genotyping in humans enriched with the estimation of heteroplasmies.

MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing.

MOTIVATION: The increasing availability of mitochondria-targeted and off-target sequencing data in whole-exome and whole-genome sequencing studies (WXS and WGS) has risen the demand of effective pipelines to accurately measure heteroplasmy and to easily recognize the most functionally important mitochondrial variants among a huge number of candidates. To this purpose, we developed MToolBox, a highly automated pipeline to reconstruct and analyze human mitochondrial DNA from high-throughput sequencing data. RESULTS: MToolBox implements an effective computational strategy for mitochondrial genomes assembling and haplogroup assignment also including a prioritization analysis of detected variants. MToolBox provides a Variant Call Format file featuring, for the first time, allele-specific heteroplasmy and annotation files with prioritized variants. MToolBox was tested on simulated samples and applied on 1000 Genomes WXS datasets. AVAILABILITY AND IMPLEMENTATION: MToolBox package is available at https://sourceforge.net/projects/mtoolbox/.