TSSV: a tool for characterization of complex allelic variants in pure and mixed genomes.

MOTIVATION: Advances in sequencing technologies and computational algorithms have enabled the study of genomic variants to dissect their functional consequence. Despite this unprecedented progress, current tools fail to reliably detect and characterize more complex allelic variants, such as short tandem repeats (STRs). We developed TSSV as an efficient and sensitive tool to specifically profile all allelic variants present in targeted loci. Based on its design, requiring only two short flanking sequences, TSSV can work without the use of a complete reference sequence to reliably profile highly polymorphic, repetitive or uncharacterized regions. RESULTS: We show that TSSV can accurately determine allelic STR structures in mixtures with 10% representation of minor alleles or complex mixtures in which a single STR allele is shared. Furthermore, we show the universal utility of TSSV in two other independent studies: characterizing de novo mutations introduced by transcription activator-like effector nucleases (TALENs) and profiling the noise and systematic errors in an IonTorrent sequencing experiment. TSSV complements the existing tools by aiding the study of highly polymorphic and complex regions and provides a high-resolution map that can be used in a wide range of applications, from personal genomics to forensic analysis and clinical diagnostics. AVAILABILITY AND IMPLEMENTATION: We have implemented TSSV as a Python package that can be installed through the command-line using pip install TSSV command. Its source code and documentation are available at https://pypi.python.org/pypi/tssv and http://www.lgtc.nl/tssv.

Application of a probabilistic genotyping software to MPS mixture STR data is supported by similar trends in LRs compared with CE data.

The interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out, for instance from sample degradation. Mixture interpretation can be improved by increasing the number of STRs and/or loci with a higher discriminatory power. Both capillary electrophoresis (CE, 6-dye) and massively parallel sequencing (MPS) provide a platform for analysing relatively large numbers of autosomal STRs. In addition, MPS enables distinguishing between sequence variants, resulting in enlarged discriminatory power. Also, MPS allows for small amplicon sizes for all loci as spacing is not an issue, which is beneficial with degraded DNA. Altogether, MPS has the potential to increase the weights of evidence for true contributors to (complex) DNA profiles. In this study, likelihood ratio (LR) calculations were performed using STR profiles obtained with two different MPS systems and analysed using different settings: 1) MPS PowerSeq™ Auto System profiles analysed using FDSTools equipped with optimized settings such as noise correction, 2) ForenSeq™ DNA Signature Prep Kit profiles analysed using the default settings in the Universal Analysis Software (UAS), and 3) ForenSeq™ DNA Signature Prep Kit profiles analysed using FDSTools empirically adapted to cope with one-directional reads and provisional, basic settings. The LR calculations used genotyping data for two- to four-person mixtures varying for mixture proportion, level of drop-out and allele sharing and were generated with the continuous model EuroForMix. The LR results for the over 2000 sets of propositions were affected by the variation for the number of markers and analysis settings used in the three approaches. Nevertheless, trends for true and non-contributors, effects of replicates, assigned number of contributors, and model validation results were comparable for the three MPS approaches and alike the trends known for CE data. Based on this analogy, we regard the probabilistic interpretation of MPS STR data fit for forensic DNA casework. In addition, guidelines were derived on when to apply LR calculations to MPS autosomal STR data and report the corresponding results.

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing.

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform.

Taxonomic classification and abundance estimation using 16S and WGS-A comparison using controlled reference samples.

The assessment of microbiome biodiversity is the most common application of metagenomics. While 16S sequencing remains standard procedure for taxonomic profiling of metagenomic data, a growing number of studies have clearly demonstrated biases associated with this method. By using Whole Genome Shotgun sequencing (WGS) metagenomics, most of the known restrictions associated with 16S data are alleviated. However, due to the computationally intensive data analyses and higher sequencing costs, WGS based metagenomics remains a less popular option. Selecting the experiment type that provides a comprehensive, yet manageable amount of information is a challenge encountered in many metagenomics studies. In this work, we created a series of artificial bacterial mixes, each with a different distribution of skin-associated microbial species. These mixes were used to estimate the resolution of two different metagenomic experiments - 16S and WGS - and to evaluate several different bioinformatics approaches for taxonomic read classification. In all test cases, WGS approaches provide much more accurate results, in terms of taxa prediction and abundance estimation, in comparison to those of 16S. Furthermore, we demonstrate that a 16S dataset, analysed using different state of the art techniques and reference databases, can produce widely different results. In light of the fact that most forensic metagenomic analysis are still performed using 16S data, our results are especially important.

Diagnostics of short tandem repeat expansion variants using massively parallel sequencing and componential tools.

Short tandem repeats (STRs) are scattered throughout the human genome. Some STRs, like trinucleotide repeat expansion (TRE) variants, cause hereditable disorders. Unambiguous molecular diagnostics of TRE disorders is hampered by current technical limitations imposed by traditional PCR and DNA sequencing methods. Here we report a novel pipeline for TRE variant diagnosis employing the massively parallel sequencing (MPS) combined with an opensource software package (FDSTools), which together are designed to distinguish true STR sequences from STR sequencing artifacts. We show that this approach can improve TRE diagnosis, such as Oculopharyngeal muscular dystrophy (OPMD). OPMD is caused by a trinucleotide expansion in the PABPN1 gene. A short GCN expansion, (GCN[10]), coding for a 10 alanine repeat is not pathogenic, but an alanine expansion is pathogenic. Applying this novel procedure in  a Dutch OPMD patient cohort, we found expansion variants from GCN[11] to GCN[16], with the GCN[16] as the most abundant variant. The repeat expansion length did not correlate with clinical features. However, symptom severity was found to correlate with age and with the initial affected muscles, suggesting that aging and muscle-specific factors can play a role in modulating OPMD.

Short hypervariable microhaplotypes: A novel set of very short high discriminating power loci without stutter artefacts.

Since two decades, short tandem repeats (STRs) are the preferred markers for human identification, routinely analysed by fragment length analysis. Here we present a novel set of short hypervariable autosomal microhaplotypes (MH) that have four or more SNPs in a span of less than 70 nucleotides (nt). These MHs display a discriminating power approaching that of STRs and provide a powerful alternative for the analysis;1;is of forensic samples that are problematic when the STR fragment size range exceeds the integrity range of severely degraded DNA or when multiple donors contribute to an evidentiary stain and STR stutter artefacts complicate profile interpretation. MH typing was developed using the power of massively parallel sequencing (MPS) enabling new powerful, fast and efficient SNP-based approaches. MH candidates were obtained from queries in data of the 1000 Genomes, and Genome of the Netherlands (GoNL) projects. Wet-lab analysis of 276 globally dispersed samples and 97 samples of nine large CEPH families assisted locus selection and corroboration of informative value. We infer that MHs represent an alternative marker type with good discriminating power per locus (allowing the use of a limited number of loci), small amplicon sizes and absence of stutter artefacts that can be especially helpful when unbalanced mixed samples are submitted for human identification.

FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise.

Massively parallel sequencing (MPS) is on the advent of a broad scale application in forensic research and casework. The improved capabilities to analyse evidentiary traces representing unbalanced mixtures is often mentioned as one of the major advantages of this technique. However, most of the available software packages that analyse forensic short tandem repeat (STR) sequencing data are not well suited for high throughput analysis of such mixed traces. The largest challenge is the presence of stutter artefacts in STR amplifications, which are not readily discerned from minor contributions. FDSTools is an open-source software solution developed for this purpose. The level of stutter formation is influenced by various aspects of the sequence, such as the length of the longest uninterrupted stretch occurring in an STR. When MPS is used, STRs are evaluated as sequence variants that each have particular stutter characteristics which can be precisely determined. FDSTools uses a database of reference samples to determine stutter and other systemic PCR or sequencing artefacts for each individual allele. In addition, stutter models are created for each repeating element in order to predict stutter artefacts for alleles that are not included in the reference set. This information is subsequently used to recognise and compensate for the noise in a sequence profile. The result is a better representation of the true composition of a sample. Using Promega Powerseq™ Auto System data from 450 reference samples and 31 two-person mixtures, we show that the FDSTools correction module decreases stutter ratios above 20% to below 3%. Consequently, much lower levels of contributions in the mixed traces are detected. FDSTools contains modules to visualise the data in an interactive format allowing users to filter data with their own preferred thresholds.

Massively parallel sequencing of short tandem repeats-Population data and mixture analysis results for the PowerSeq™ system.

Current forensic DNA analysis predominantly involves identification of human donors by analysis of short tandem repeats (STRs) using Capillary Electrophoresis (CE). Recent developments in Massively Parallel Sequencing (MPS) technologies offer new possibilities in analysis of STRs since they might overcome some of the limitations of CE analysis. In this study 17 STRs and Amelogenin were sequenced in high coverage using a prototype version of the Promega PowerSeq™ system for 297 population samples from the Netherlands, Nepal, Bhutan and Central African Pygmies. In addition, 45 two-person mixtures with different minor contributions down to 1% were analysed to investigate the performance of this system for mixed samples. Regarding fragment length, complete concordance between the MPS and CE-based data was found, marking the reliability of MPS PowerSeq™ system. As expected, MPS presented a broader allele range and higher power of discrimination and exclusion rate. The high coverage sequencing data were used to determine stutter characteristics for all loci and stutter ratios were compared to CE data. The separation of alleles with the same length but exhibiting different stutter ratios lowers the overall variation in stutter ratio and helps in differentiation of stutters from genuine alleles in mixed samples. All alleles of the minor contributors were detected in the sequence reads even for the 1% contributions, but analysis of mixtures below 5% without prior information of the mixture ratio is complicated by PCR and sequencing artefacts.