Determining the underlying haplotypes of individual human genomes is an essential, but currently difficult, step toward a complete understanding of genome function. Fosmid pool-based next-generation sequencing allows genome-wide generation of 40-kb haploid DNA segments, which can be phased into contiguous molecular haplotypes computationally by Single Individual Haplotyping (SIH). Many SIH algorithms have been proposed, but the accuracy of such methods has been difficult to assess due to the lack of real benchmark data. To address this problem, we generated whole genome fosmid sequence data from a HapMap trio child, NA12878, for which reliable haplotypes have already been produced. We assembled haplotypes using eight algorithms for SIH and carried out direct comparisons of their accuracy, completeness and efficiency. Our comparisons indicate that fosmid-based haplotyping can deliver highly accurate results even at low coverage and that our SIH algorithm, ReFHap, is able to efficiently produce high-quality haplotypes. We expanded the haplotypes for NA12878 by combining the current haplotypes with our fosmid-based haplotypes, producing near-to-complete new gold-standard haplotypes containing almost 98% of heterozygous SNPs. This improvement includes notable fractions of disease-related and GWA SNPs. Integrated with other molecular biological data sets, this phase information will advance the emerging field of diploid genomics.

/pdf/fosmid-based-whole-genome-haplotyping-of-a-hapmap-trio-child-47es3ufglt.pdf

Fosmid-based whole genome haplotyping of a HapMap trio child: evaluation of Single Individual Haplotyping techniques.

Motivation: Single nucleotide polymorphisms are the most common form of variation in human DNA, and are involved in many research fields, from molecular biology to medical therapy. The technological opportunity to deal with long DNA sequences using shotgun sequencing has raised the problem of fragment recombination. In this regard, Single Individual Haplotyping (SIH) problem has received considerable attention over the past few years.

Results: In this article, we survey seven recent approaches to the SIH problem and evaluate them extensively using real human haplotype data from the HapMap project. We also implemented a data generator tailored to the current shotgun sequencing technology that uses haplotypes from the HapMap project.

Availability: The data we used to compare the algorithms are available on demand, since we think they represent an important benchmark that can be used to easily compare novel algorithmic ideas with the state of the art. Moreover, we had to re-implement six of the algorithms surveyed because the original code was not available to us. Five of these algorithms and the data generator used in this article endowed with a Web interface are available at http://bioalgo.iit.cnr.it/rehap

Contact: filippo.geraci@iit.cnr.it

A comparison of several algorithms for the single individual SNP haplotyping reconstruction problem

Full human genomic sequences have been published in the latest two years for a growing number of individuals. Most of them are a mixed consensus of the two real haplotypes because it is still very expensive to separate information coming from the two copies of a chromosome. However, latest improvements and new experimental approaches promise to solve these issues and provide enough information to reconstruct the sequences for the two copies of each chromosome through bioinformatics methods such as single individual haplotyping. Full haploid sequences provide a complete understanding of the structure of the human genome, allowing accurate predictions of translation in protein coding regions and increasing power of association studies.In this paper we present a novel problem formulation for single individual haplotyping. We start by assigning a score to each pair of fragments based on their common allele calls and then we use these score to formulate the problem as the cut of fragments that maximize an objective function, similar to the well known max-cut problem. Our algorithm initially finds the best cut based on a heuristic algorithm for max-cut and then builds haplotypes consistent with that cut. We have compared both accuracy and running time of ReFHap with other heuristic methods on both simulated and real data and found that ReFHap performs significantly faster than previous methods without loss of accuracy.

/pdf/refhap-a-reliable-and-fast-algorithm-for-single-individual-2856wo2r78.pdf

ReFHap: a reliable and fast algorithm for single individual haplotyping

Author(s): Xie, Minzhu; Wu, Qiong; Wang, Jianxin; Jiang, Tao | Abstract: MotivationSome economically important plants including wheat and cotton have more than two copies of each chromosome. With the decreasing cost and increasing read length of next-generation sequencing technologies, reconstructing the multiple haplotypes of a polyploid genome from its sequence reads becomes practical. However, the computational challenge in polyploid haplotyping is much greater than that in diploid haplotyping, and there are few related methods.ResultsThis article models the polyploid haplotyping problem as an optimal poly-partition problem of the reads, called the Polyploid Balanced Optimal Partition model. For the reads sequenced from a k-ploid genome, the model tries to divide the reads into k groups such that the difference between the reads of the same group is minimized while the difference between the reads of different groups is maximized. When the genotype information is available, the model is extended to the Polyploid Balanced Optimal Partition with Genotype constraint problem. These models are all NP-hard. We propose two heuristic algorithms, H-PoP and H-PoPG, based on dynamic programming and a strategy of limiting the number of intermediate solutions at each iteration, to solve the two models, respectively. Extensive experimental results on simulated and real data show that our algorithms can solve the models effectively, and are much faster and more accurate than the recent state-of-the-art polyploid haplotyping algorithms. The experiments also show that our algorithms can deal with long reads and deep read coverage effectively and accurately. Furthermore, H-PoP might be applied to help determine the ploidy of an organism.Availability and implementationhttps://github.com/MinzhuXie/H-PoPG CONTACT: xieminzhu@hotmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

/pdf/h-pop-and-h-popg-heuristic-partitioning-algorithms-for-5gc9z3eqgt.pdf

H-PoP and H-PoPG: heuristic partitioning algorithms for single individual haplotyping of polyploids.

Gene duplication is an important evolutionary mechanism allowing to provide new genetic material and thus opportunities to acquire new gene functions for an organism, with major implications such as speciation events. Various processes are known to allow a gene to be duplicated and different models explain how duplicated genes can be maintained in genomes. Due to their particular importance, the identification of duplicated genes is essential when studying genome evolution but it can still be a challenge due to the various fates duplicated genes can encounter. In this review, we first describe the evolutionary processes allowing the formation of duplicated genes but also describe the various bioinformatic approaches that can be used to identify them in genome sequences. Indeed, these bioinformatic approaches differ according to the underlying duplication mechanism. Hence, understanding the specificity of the duplicated genes of interest is a great asset for tool selection and should be taken into account when exploring a biological question.

An Overview of Duplicated Gene Detection Methods: Why the Duplication Mechanism Has to Be Accounted for in Their Choice.

Single nucleotide polymorphism (SNP) is the most frequent form of DNA variation. The set of SNP's present in a chromosome (called the em haplotype) is of interest in a wide area of applications in molecular biology and biomedicine, including diagnostic and medical therapy. In this paper we propose a new heuristic method for the problem of haplotype reconstruction for (portions of) a pair of homologous human chromosomes from a single individual (SIH). The problem is well known in literature and exact algorithms have been proposed for the case when no (or few) gaps are allowed in the input fragments. These algorithms, though exact and of polynomial complexity, are slow in practice. When gaps are considered no exact method of polynomial complexity is known. The problem is also hard to approximate with guarantees. Therefore fast heuristics have been proposed. In this paper we describe SpeedHap, a new heuristic method that is able to tackle the case of many gapped fragments and retains its effectiveness even when the input fragments have high rate of reading errors (up to 20%) and low coverage (as low as 3). We test SpeedHap on real data from the HapMap Project.

/pdf/speedhap-an-accurate-heuristic-for-the-single-individual-snp-tmsxn019pm.pdf

SpeedHap: An Accurate Heuristic for the Single Individual SNP Haplotyping Problem with Many Gaps, High Reading Error Rate and Low Coverage

Single nucleotide polymorphism (SNP) is the most frequent form of DNA variation. The set of SNPs present in a chromosome (called the haplotype) is of interest in a wide area of applications in molecular biology and biomedicine, including diagnostic and medical therapy. In this paper we propose a new heuristic method for the problem of haplotype reconstruction for (portions of ) a pair of homologous human chromosomes from a single individual (SIH). The problem is well known in literature and exact algorithms have been proposed for the case when no (or few) gaps are allowed in the input fragments. These algorithms, though exact and of polynomial complexity, are slow in practice. Therefore fast heuristics have been proposed. In this paper we describe a new heuristic method that is able to tackle the case of many gapped fragments and retains its effectiveness even when the input fragments have high rate of reading errors (up to 20%) and low coverage (as low as 3). We test our method on real data from the HapMap Project.

A fast and accurate heuristic for the single individual snp haplotyping problem with many gaps, high reading error rate and low coverage

Motivation Large-scale sequencing projects have confirmed the hypothesis that eukaryotic DNA is rich in repetitions whose functional role needs to be elucidated. In particular, tandem repeats (TRs) (i.e. short, almost identical sequences that lie adjacent to each other) have been associated to many cellular processes and, indeed, are also involved in several genetic disorders. The need of comprehensive lists of TRs for association studies and the absence of a computational model able to capture their variability have revived research on discovery algorithms. Results Building upon the idea that sequence similarities can be easily displayed using graphical methods, we formalized the structure that TRs induce in dot-plot matrices where a sequence is compared with itself. Leveraging on the observation that a compact representation of these matrices can be built and searched in linear time, we developed Dot2dot: an accurate algorithm fast enough to be suitable for whole-genome discovery of TRs. Experiments on five manually curated collections of TRs have shown that Dot2dot is more accurate than other established methods, and completes the analysis of the biggest known reference genome in about one day on a standard PC. Availability and implementation Source code and datasets are freely available upon paper acceptance at the URL: https://github.com/Gege7177/Dot2dot. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/dot2dot-accurate-whole-genome-tandem-repeats-discovery-2snhwg174b.pdf

Dot2dot: accurate whole-genome tandem repeats discovery

MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3' UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation.

/pdf/genome-wide-analysis-of-ngs-data-to-compile-cancer-specific-4zdwzqywy4.pdf

Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers.

Polymorphic Tandem Repeat (PTR) is a common form of polymorphism in the human genome. A PTR consists in a variation found in an individual (or in a population) of the number of repeating units of a Tandem Repeat (TR) locus of the genome with respect to the reference genome. Several phenotypic traits and diseases have been discovered to be strongly associated with or caused by specific PTR loci. PTR are further distinguished in two main classes: Short Tandem Repeats (STR) when the repeating unit has size up to 6 base pairs, and Variable Number Tandem Repeats (VNTR) for repeating units of size above 6 base pairs. As larger and larger populations are screened via high throughput sequencing projects, it becomes technically feasible and desirable to explore the association between PTR and a panoply of such traits and conditions. In order to facilitate these studies, we have devised a method for compiling catalogues of PTR from assembled genomes, and we have produced a catalogue of PTR for genic regions (exons, introns, UTR and adjacent regions) of the human genome (GRCh38). We applied four different TR discovery software tools to uncover in the first phase 55,223,485 TR (after duplicate removal) in GRCh38, of which 373,173 were determined to be PTR in the second phase by comparison with five assembled human genomes. Of these, 263,266 are not included by state-of-the-art PTR catalogues. The new methodology is mainly based on a hierarchical and systematic application of alignment-based sequence comparisons to identify and measure the polymorphism of TR. While previous catalogues focus on the class of STR of small total size, we remove any size restrictions, aiming at the more general class of PTR, and we also target fuzzy TR by using specific detection tools. Similarly to other previous catalogues of human polymorphic loci, we focus our catalogue towards applications in the discovery of disease-associated loci. Validation by cross-referencing with existing catalogues on common clinically-relevant loci shows good concordance. Overall, this proposed census of human PTR in genic regions is a shared resource (web accessible), complementary to existing catalogues, facilitating future genome-wide studies involving PTR.

/pdf/a-census-of-tandemly-repeated-polymorphic-loci-in-genic-stce2ucije.pdf

Loredana M. Genovese

Papers

SpeedHap: An Accurate Heuristic for the Single Individual SNP Haplotyping Problem with Many Gaps, High Reading Error Rate and Low Coverage

A fast and accurate heuristic for the single individual snp haplotyping problem with many gaps, high reading error rate and low coverage

Dot2dot: accurate whole-genome tandem repeats discovery

Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers.

A Census of Tandemly Repeated Polymorphic Loci in Genic Regions Through the Comparative Integration of Human Genome Assemblies.