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1. Haplotype based testing for a better understanding of the selective architecture

   https://doi.org/10.1186/s12859-023-05437-3  

   Chen, Haoyu ; Pelizzola, Marta ; Futschik, Andreas ( August 2023 , BMC Bioinformatics)

   The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies.

   Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits.

   Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

    

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2. De novo haplotype reconstruction in viral quasispecies using paired-end read guided path finding

   https://doi.org/10.1093/bioinformatics/bty202  

   Chen, Jiao ; Zhao, Yingchao ; Sun, Yanni ; Hancock, ed., John ( April 2018 , Bioinformatics)

   RNA virus populations contain different but genetically related strains, all infecting an individual host. Reconstruction of the viral haplotypes is a fundamental step to characterize the virus population, predict their viral phenotypes and finally provide important information for clinical treatment and prevention. Advances of the next-generation sequencing technologies open up new opportunities to assemble full-length haplotypes. However, error-prone short reads, high similarities between related strains, an unknown number of haplotypes pose computational challenges for reference-free haplotype reconstruction. There is still much room to improve the performance of existing haplotype assembly tools.

   In this work, we developed a de novo haplotype reconstruction tool named PEHaplo, which employs paired-end reads to distinguish highly similar strains for viral quasispecies data. It was applied on both simulated and real quasispecies data, and the results were benchmarked against several recently published de novo haplotype reconstruction tools. The comparison shows that PEHaplo outperforms the benchmarked tools in a comprehensive set of metrics.

   The source code and the documentation of PEHaplo are available at https://github.com/chjiao/PEHaplo.

   Supplementary data are available at Bioinformatics online.

    

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3. COLD-PCR Enrichment of Rare Cancer Mutations prior to Targeted Amplicon Resequencing

   https://doi.org/10.1373/clinchem.2011.176198  

   Milbury, Coren A. ; Correll, Mick ; Quackenbush, John ; Rubio, Renee ; Makrigiorgos, G. Mike ( January 2020 , Clinical Chemistry)

   Despite widespread interest in next-generation sequencing (NGS), the adoption of personalized clinical genomics and mutation profiling of cancer specimens is lagging, in part because of technical limitations. Tumors are genetically heterogeneous and often contain normal/stromal cells, features that lead to low-abundance somatic mutations that generate ambiguous results or reside below NGS detection limits, thus hindering the clinical sensitivity/specificity standards of mutation calling. We applied COLD-PCR (coamplification at lower denaturation temperature PCR), a PCR methodology that selectively enriches variants, to improve the detection of unknown mutations before NGS-based amplicon resequencing.

   We used both COLD-PCR and conventional PCR (for comparison) to amplify serially diluted mutation-containing cell-line DNA diluted into wild-type DNA, as well as DNA from lung adenocarcinoma and colorectal cancer samples. After amplification of TP53 (tumor protein p53), KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), IDH1 [isocitrate dehydrogenase 1 (NADP+), soluble], and EGFR (epidermal growth factor receptor) gene regions, PCR products were pooled for library preparation, bar-coded, and sequenced on the Illumina HiSeq 2000.

   In agreement with recent findings, sequencing errors by conventional targeted-amplicon approaches dictated a mutation-detection limit of approximately 1%–2%. Conversely, COLD-PCR amplicons enriched mutations above the error-related noise, enabling reliable identification of mutation abundances of approximately 0.04%. Sequencing depth was not a large factor in the identification of COLD-PCR–enriched mutations. For the clinical samples, several missense mutations were not called with conventional amplicons, yet they were clearly detectable with COLD-PCR amplicons. Tumor heterogeneity for the TP53 gene was apparent.

   As cancer care shifts toward personalized intervention based on each patient's unique genetic abnormalities and tumor genome, we anticipate that COLD-PCR combined with NGS will elucidate the role of mutations in tumor progression, enabling NGS-based analysis of diverse clinical specimens within clinical practice.

    

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4. TopHap: rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity

   https://doi.org/10.1093/bioinformatics/btac186  

   Caraballo-Ortiz, Marcos A. ; Miura, Sayaka ; Sanderford, Maxwell ; Dolker, Tenzin ; Tao, Qiqing ; Weaver, Steven ; Pond, Sergei L. K. ; Kumar, Sudhir ; Schwartz, ed., Russell ( March 2022 , Bioinformatics)

   Building reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites but millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate and fast phylogenetic inference of resolvable phylogenetic features.

   We present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. We develop a bootstrap strategy that resamples genomes spatiotemporally to assess topological robustness. The application of TopHap to build a phylogeny of 68 057 SARS-CoV-2 genomes (68KG) from the first year of the pandemic produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million SARS-CoV-2 genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major and recent variants of concern.

   TopHap is available at https://github.com/SayakaMiura/TopHap.

   Supplementary data are available at Bioinformatics online.

    

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5. A rapid and label-free platform for virus capture and identification from clinical samples

   https://doi.org/10.1073/pnas.1910113117  

   Yeh, Yin-Ting ; Gulino, Kristen ; Zhang, YuHe ; Sabestien, Aswathy ; Chou, Tsui-Wen ; Zhou, Bin ; Lin, Zhong ; Albert, Istvan ; Lu, Huaguang ; Swaminathan, Venkataraman ; et al ( January 2020 , Proceedings of the National Academy of Sciences)

   Emerging and reemerging viruses are responsible for a number of recent epidemic outbreaks. A crucial step in predicting and controlling outbreaks is the timely and accurate characterization of emerging virus strains. We present a portable microfluidic platform containing carbon nanotube arrays with differential filtration porosity for the rapid enrichment and optical identification of viruses. Different emerging strains (or unknown viruses) can be enriched and identified in real time through a multivirus capture component in conjunction with surface-enhanced Raman spectroscopy. More importantly, after viral capture and detection on a chip, viruses remain viable and get purified in a microdevice that permits subsequent in-depth characterizations by various conventional methods. We validated this platform using different subtypes of avian influenza A viruses and human samples with respiratory infections. This technology successfully enriched rhinovirus, influenza virus, and parainfluenza viruses, and maintained the stoichiometric viral proportions when the samples contained more than one type of virus, thus emulating coinfection. Viral capture and detection took only a few minutes with a 70-fold enrichment enhancement; detection could be achieved with as little as 10 2 EID 50 /mL (50% egg infective dose per microliter), with a virus specificity of 90%. After enrichment using the device, we demonstrated by sequencing that the abundance of viral-specific reads significantly increased from 4.1 to 31.8% for parainfluenza and from 0.08 to 0.44% for influenza virus. This enrichment method coupled to Raman virus identification constitutes an innovative system that could be used to quickly track and monitor viral outbreaks in real time. 

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