Low Sequencing Depth Research Articles

A comparison of alternative mRNA splicing in the CD4 and CD8 T cell lineages

T cells can be subdivided into a number of different subsets that are defined by their distinct functions. While the specialization of different T cell subsets is partly achieved by the expression of specific genes, the overall transcriptional profiles of all T cells appear very similar. Alternative mRNA splicing is a mechanism that facilitates greater transcript/protein diversity from a limited number of genes, which may contribute to the functional specialization of distinct T cell subsets. In this study we employ a combination of short-read and long-read sequencing technologies to compare alternative mRNA splicing between the CD4 and CD8 T cell lineages. While long-read technology was effective at assembling full-length alternatively spliced transcripts, the low sequencing depth did not facilitate accurate quantitation. On the other hand, short-read technology was ineffective at assembling full-length transcripts but was highly accurate for quantifying expression. We show that integrating long-read and short-read data together achieves a more complete view of transcriptomic diversity. We found that while the overall usage of transcript isoforms was very similar between the CD4 and CD8 lineages, there were numerous alternative spliced mRNA isoforms that were preferentially used by one lineage over the other. These alternative spliced isoforms included ones with different exon usage, exon exclusion or intron inclusion, all of which are expected to significantly alter the protein sequence.

Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy.

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low-depth sequencing across a larger fraction of the genome than with high-depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

A reference-free approach to analyse RADseq data using standard next generation sequencing toolkits.

Genotyping-by-sequencing methods such as RADseq are popular for generating genomic and population-scale data sets from a diverse range of organisms. These often lack a usable reference genome, restricting users to RADseq specific software for processing. However, these come with limitations compared to generic next generation sequencing (NGS) toolkits. Here, we describe and test a simple pipeline for reference-free RADseq data processing that blends de novo elements from STACKS with the full suite of state-of-the art NGS tools. Specifically, we use the de novo RADseq assembly employed by STACKS to create a catalogue of RAD loci that serves as a reference for read mapping, variant calling and site filters. Using RADseq data from 28 zebra sequenced to ~8x depth-of-coverage we evaluate our approach by comparing the site frequency spectra (SFS) to those from alternative pipelines. Most pipelines yielded similar SFS at 8x depth, but only a genotype likelihood based pipeline performed similarly at low sequencing depth (2-4x). We compared the RADseq SFS with medium-depth (~13x) shotgun sequencing of eight overlapping samples, revealing that the RADseq SFS was persistently slightly skewed towards rare and invariant alleles. Using simulations and human data we confirm that this is expected when there is allelic dropout (AD) in the RADseq data. AD in the RADseq data caused a heterozygosity deficit of ~16%, which dropped to ~5% after filtering AD. Hence, AD was the most important source of bias in our RADseq data.

MITGARD: an automated pipeline for mitochondrial genome assembly in eukaryotic species using RNA-seq data.

Over the past decade, the field of next-generation sequencing (NGS) has seen dramatic advances in methods and a decrease in costs. Consequently, a large expansion of data has been generated by NGS, most of which have originated from RNA-sequencing (RNA-seq) experiments. Because mitochondrial genes are expressed in most eukaryotic cells, mitochondrial mRNA sequences are usually co-sequenced within the target transcriptome, generating data that are commonly underused or discarded. Here, we present MITGARD, an automated pipeline that reliably recovers the mitochondrial genome from RNA-seq data from various sources. The pipeline identifies mitochondrial sequence reads based on a phylogenetically related reference, assembles them into contigs, and extracts a complete mtDNA for the target species. We demonstrate that MITGARD can reconstruct the mitochondrial genomes of several species throughout the tree of life. We noticed that MITGARD can recover the mitogenomes in different sequencing schemes and even in a scenario of low-sequencing depth. Moreover, we showed that the use of references from congeneric species diverging up to 30 million years ago (MYA) from the target species is sufficient to recover the entire mitogenome, whereas the use of species diverging between 30 and 60 MYA allows the recovery of most mitochondrial genes. Additionally, we provide a case study with original data in which we estimate a phylogenetic tree of snakes from the genus Bothrops, further demonstrating that MITGARD is suitable for use on biodiversity projects. MITGARD is then a valuable tool to obtain high-quality information for studies focusing on the phylogenetic and evolutionary aspects of eukaryotes and provides data for easily identifying a sample using barcoding, and to check for cross-contamination using third-party tools.

A Zero-Inflated Latent Dirichlet Allocation Model for Microbiome Studies.

The human microbiome consists of a community of microbes in varying abundances and is shown to be associated with many diseases. An important first step in many microbiome studies is to identify possible distinct microbial communities in a given data set and to identify the important bacterial taxa that characterize these communities. The data from typical microbiome studies are high dimensional count data with excessive zeros due to both absence of species (structural zeros) and low sequencing depth or dropout. Although methods have been developed for identifying the microbial communities based on mixture models of counts, these methods do not account for excessive zeros observed in the data and do not differentiate structural from sampling zeros. In this paper, we introduce a zero-inflated Latent Dirichlet Allocation model (zinLDA) for sparse count data observed in microbiome studies. zinLDA builds on the flexible Latent Dirichlet Allocation model and allows for zero inflation in observed counts. We develop an efficient Markov chain Monte Carlo (MCMC) sampling procedure to fit the model. Results from our simulations show zinLDA provides better fits to the data and is able to separate structural zeros from sampling zeros. We apply zinLDA to the data set from the American Gut Project and identify microbial communities characterized by different bacterial genera.

Assembling mitogenome of Himalayan Black Bear (U. t. laniger) from low depth reads and its application in drawing phylogenetic inferences

The complete mitogenome of Himalayan black bear (Ursus thibetanus laniger) from Indian Himalayan region was assembled following the modified approach of mitochondrial baiting and mapping using the next-generation sequencing reads. The complete mitogenome was of 16,556 bp long, consisted of 37 genes that contained 13 protein-coding genes, 22 tRNAs, 2 rRNAs and 1 control region. The complete base composition was 31.33% A, 15.24% G, 25.45%C, and 27.98%T and gene arrangement was similar to the other sub-species of Asiatic black bear. The relative synonymous codon usage analysis revealed the maximum abundance of Isoleucine, Tyrosine, Leucine and Threonine. The assembled mitogenome of U. t. laniger exhibited 99% similarity with the mitogenomes of Himalayan black bear available from Nepal and Tibetan Plateau-Himalaya region. The findings of the present study has proven low depth sequencing data, adequate and highly efficient in rapid recovering the mitochondrial genome by overcoming the conventional strategies of obtaining long-range PCR and subsequently drawing phylogenetic inferences.

ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin.

Cell-free DNA in human plasma provides access to molecular information about the pathological processes in the organs or tumors from which it originates. These DNA fragments are derived from fragmented chromatin in dying cells, and retain some of the cell of origin histone modifications. Here, we apply chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 268 human samples. In healthy donors, we identified bone marrow megakaryocytes, but not erythroblasts, as major contributors to the cfDNA pool. In patients with a range of liver diseases, we show that we can identify pathology- related changes in hepatocyte transcriptional programs. In metastatic colorectal carcinoma patients, we detected clinically relevant, and patient-specific information, including transcriptionally active HER2 amplifications. Altogether, cfChIP-seq using low sequencing depth, provides systemic and genome-wide information, can inform diagnosis, and facilitate interrogation of physiological and pathological processes using blood samples.

Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

Oxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9–99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

Identification of Circular RNAs by Multiple Displacement Amplification and Their Involvement in Plant Development.

With the innovative knowledge and bioinformatics tools in the identification and characterization of noncoding RNAs, circular RNA (circRNA) is added as a new member to the noncoding RNAs family. CircRNA enrichment by rRNA depletion/RNase R or poly-A removal/RNase R treatment followed by NGS analysis is the most frequently adopted method for circular RNA identification and characterization. In this chapter, we describe the multiple displacement amplification (MDA) as a convenient method to augment the identification of even the abysmally expressed circular RNAs at low sequencing depth. Total RNA, extracted at three different developmental stages of rice, is subjected to RiboMinus and RNase R treatment to deplete the linear RNAs. The enriched circular RNAs are reverse transcribed with random hexamers. The resulting cDNA is subjected to phi29 DNA polymerase amplification using exo-resistant random pentamers to yield high molecular weight dsDNA product, followed by Illumina sequencing at ten million paired end reads per sample. The sequence analysis yielded a promising number of circRNAs with the appreciable inclusion of differentially regulated and minimally expressed circRNAs at a comparatively reduced cost.

Simultaneous Tagmentation-Based Detection of ChIP/ATAC Signal with Bisulfite Sequencing.

DNA methylation is thought to regulate accessibility of chromatin and binding of regulatory elements; however, it is difficult to determine if chromatin accessibility or transcription factor (TF) binding overlap with methylated or unmethylated DNA if the assays are performed separately. In order to examine accessibility or TF binding simultaneously with methylation on the same DNA molecule, we developed EpiMethylTag which combines ATAC-Seq or ChIP-Seq (M-ATAC or M-ChIP) with bisulfite conversion. Our approach provides a fast, low-input, low sequencing depth method to determine whether DNAme and accessibility/TF binding are mutually exclusive or can coexist in certain locations.

LiBis: an ultrasensitive alignment augmentation for low-input bisulfite sequencing.

The cell-free DNA (cfDNA) methylation profile in liquid biopsy has been utilized to diagnose early-stage disease and estimate therapy response. However, typical clinical procedures are capable of purifying only very small amounts of cfDNA. Whole-genome bisulfite sequencing (WGBS) is the gold standard for measuring DNA methylation; however, WGBS using small amounts of fragmented DNA introduces a critical challenge for data analysis, namely a low-mapping ratio. The resulting low sequencing depth and low coverage of CpG sites genome-wide is a bottleneck for the clinical application of cfDNA-based WGBS assays. We developed LiBis (Low-input Bisulfite Sequencing), a novel method for low-input WGBS data alignment. By dynamically clipping initially unmapped reads and remapping clipped fragments, we judiciously rescued those reads and uniquely aligned them to the genome. By substantially increasing the mapping ratio by up to 88%, LiBis dramatically improved the number of informative CpGs and the precision in quantifying the methylation status of individual CpG sites. LiBis significantly improved the cost efficiency of low-input WGBS experiments by dynamically removing contamination introduced by random priming. The high sensitivity and cost effectiveness afforded by LiBis for low-input samples will allow the discovery of genetic and epigenetic features suitable for downstream analysis and biomarker identification using liquid biopsy.

Identification of copy number variants by NGS-based NIPT at low sequencing depth

ObjectiveTo explore the clinical utility of detecting chromosome copy number variants (CNVs) in the fetus by noninvasive prenatal testing (NIPT) using the low-pass whole-genome sequencing. MethodsEight hundred and seventy-three singleton pregnancies with chromosomal microarray analysis (CMA) available between January 2017 to December 2019 and stored enough plasma sample for NIPT testing were included in this study. The CMA results show that forty-eight pregnancies with CNVs and eight hundred and twenty-five pregnancies are normal. Each pregnancy's plasma sample was blindly tested with NIPT at a depth of 0.51-1.19x for CNVs detection. The performance of the NIPT method for CNVs detection compared with the CMA method is evaluated. ResultsA total of fifty-two CNVs ranging from 0.1–47.3 Mb identified in forty-eight samples were identified by NIPT, of which thirty-four CNVs were consistent with CMA results. Additionally, eighteen CNVs were missed by NIPT. The overall sensitivity and specificity for the detection of CNVs were 65.38% (95% CI: 51.76%-76.89%) and 97.45% (95% CI: 96.12%-98.35%), respectively. However, for the detection of CNVs larger than 2 Mb and CNVs less than 2Mb, the sensitivities were 81.58% (95% CI: 66.27%-91.09%) and 21.43% (95% CI: 6.84%-48.32%), respectively. ConclusionOur study demonstrated that the NIPT might be an alternative method for screening CNVs comparable with other studies. However, CNVs less than 2Mb in length shows poor sensitivity by NIPT. Noninvasive CNVs detection based on the NIPT method still needs more clinical validation studies and technical improvement to achieve clinically acceptable accuracy.

Characterization of universal features of partially methylated domains across tissues and species

BackgroundPartially methylated domains (PMDs) are a hallmark of epigenomes in reproducible and specific biological contexts, including cancer cells, the placenta, and cultured cell lines. Existing methods for deciding whether PMDs exist in a sample, as well as their identification, are few, often tailored to specific biological questions, and require high coverage samples for accurate identification.ResultsIn this study, we outline a set of axioms that take a step towards a functional definition for PMDs, describe an improved method for comparable PMD detection across samples with substantially differing sequencing depths, and refine the decision criteria for whether a sample contains PMDs using a data-driven approach. Applying our method to 267 methylomes from 7 species, we corroborated recent results regarding the general association between replication timing and PMD state, and report identification of several reproducibly “escapee” genes within late-replicating domains that escape the reduced expression and hypomethylation of their immediate genomic neighborhood. We also explored the discordant PMD state of orthologous genes between human and mouse, and observed a directional association of PMD state with gene expression and local gene density.ConclusionsOur improved method makes low sequencing depth, population-level studies of PMD variation possible and our results further refine the model of PMD formation as one where sequence context and regional epigenomic features both play a role in gradual genome-wide hypomethylation.

Abstract LB-245: Validating models of imputing germline versus somatic status for variants detected by tumor-only genomic profiling using germline clinical testing data

Abstract The goal of tumor genomic profiling is to identify somatic mutations. However, most implementations lack patient-matched control DNA sequence to enable determination of variant source (somatic or germline) and resolution of allelic status, including possible loss of heterozygosity (LOH), without additional analyses based on accurate estimates of specimens' tumor purity. Here, we present the validation of an information-theoretic algorithm for identifying germline variants detected in tumor-only sequencing using cancer patient clinical germline test data. 1,631 adult patients consented for the PROFILE study at DFCI between 2014 and 2018, and also underwent clinical germline testing. DNA was isolated from tissue containing >20% tumor nuclei and analyzed using Agilent SureSelect hybrid capture kit. All exons and 191 introns in 447 genes were interrogated for single nucleotide variants, small indels, copy number alterations, and structural variants using standard pipelines. Germline testing was performed at CLIA-certified laboratories. Specimen tumor purity was estimated using computational approaches developed at DFCI and Rutgers with further manual curation. LOHGIC (LOH-Germline Inference Calculator), developed on a model-selection scheme using Akaike Information Criterion weighting, was applied to individual variants to infer the most consistent germline-v-somatic model and LOH status, incorporating biases in clinical sequencing and purity estimation. 427 patients had 676 variants detected in high-penetrance cancer predisposition genes: BRCA1, BRCA2, MLH1, MSH2, MSH6 and PMS2. Comparison of LOHGIC's results with germline test data showed 91.4% accuracy, 67.6% precision, and 67.0% recall, with other performance measures reported in Table 1. LOHGIC showed evidence of LOH for 35% of variants with concordant inference and germline testing results. Special challenges with TP53 variants will also be discussed. Our results indicate that tumor-only sequencing can provide excellent power for statistical approaches to identify likely germline mutations and infer LOH. Our analyses may be confounded by the presence of reversion mutations, poor specimen quality, inaccurate purity estimates, low sequencing depths, and/or germline call variability. This work further demonstrates the need for a systematic effort to interpret clinical-grade sequencing results. Table 1.Summary of resultsGeneBRCA1BRCA2MLH1MSH2MSH6PMS2OverallGermline Test: Positive/Negative28/10445/1753/6010/6514/12112/39112/564Germline Inference: Positive/Negative/Ambiguous24/94/1448/138/347/46/1013/51/1115/105/154/37/10111/471/94Positive Predictive Value (PPV)79.270.842.969.246.775.067.6Negative Predictive Value (NPV)95.798.6100.0100.096.286.596.9True Positive Rate (TPR)67.975.6100.090.050.025.067.0False Omission Rate (FOR)4.31.40.00.03.813.53.1Sensitivity82.694.4100.0100.063.637.583.3Specificity94.790.792.092.792.797.092.8Accuracy92.491.492.593.890.085.491.4 Citation Format: Israel Gomy, Nahed Jalloul, Samantha Stokes, Alexander Gusev, Shridar Ganesan, Judy Garber, Hossein Khiabanian. Validating models of imputing germline versus somatic status for variants detected by tumor-only genomic profiling using germline clinical testing data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-245.

Abstract 4433: Comprehensive characterization of nonreference allele occurring in high throughput sequencing of tumor sample

Abstract Background: As high throughput sequencing (HTS) technique advances, HTS has been routinely used in cancer research as well as in clinical setting. However, we often face the situation where we need to identify somatic variants from tumor tissue without matched normal control. This is challenging largely due to 1) Intrinsic nature of tumor such as purity, ploidy and clonality. 2) technical error introduced by imperfect experimental procedure. 3) The complexity of human genome. 4) unavailability of matched normal to distinguish germline variants from somatic variants. Several efforts have been made to resolve this issue such as SGZ and PureCN. However, their performance remains unsatisfactory. Most of these methods focused on tumor heterogeneity inference followed by variant allele frequency (VAF) estimation while largely ignored the manifestation of variant harboring alignment. A better understanding of the characteristics of non-reference allele in NGS pipeline is essential for developing an improved variant calling and filtering approach. Therefore, we present a comprehensive evaluation of the characteristic of non-reference alleles. Method: Whole Exome Sequencing (WES) was carried out. Three types of non-reference allele were identified as following: Somatic and germline variants were identified using MuTect (with matched normal) and HaplotypeCaller respectively according to GATK best practice. For artifacts identification, Pisces is used in tumor-only-mode. The retained variants were subtracted by somatic and germline dataset. Variants with either low VAF or low sequencing depth were excluded from all three datasets. In terms of feature engineering, we defined a set of features that may differentiate between variant types including VAF, population frequency, mappability related genomic element, alignment integrity, etc. Corresponding analysis were carried out to evaluate these features individually. Result and conclusion: The above analysis can be seen as the process of feature evaluation and prioritization. we fitted a selection of features to a number of supervised classifiers including random forest, adaptive boosting, gradient boosting and voting classifier (soft voting). Precision and recall were measured based on KFold (K=5) cross validation. Random forest achieved highest performance: 0.90 and 0.93 for germline prediction. 0.93 and 0.91 for somatic prediction. 0.95 and 0.94 for artifact prediction. This result suggests that, by carefully selecting relevant features, can we achieve acceptable accuracy of variant detection even when matched normal is not available. Admittedly, we found some drawbacks alone the way of our analysis, for example some repeatedly occurred artifacts that are unaccounted by our features. Besides, our approach may be compromised when analyzing tumor with extreme purity. Further research is required to include additional features to tackle these drawbacks and achieve better performance. Citation Format: Cheng Yan, Weihua Guo, Hongling Yuan, Yufei Yang. Comprehensive characterization of nonreference allele occurring in high throughput sequencing of tumor sample [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4433.

High throughput error corrected Nanopore single cell transcriptome sequencing

Droplet-based high throughput single cell sequencing techniques tremendously advanced our insight into cell-to-cell heterogeneity. However, those approaches only allow analysis of one extremity of the transcript after short read sequencing. In consequence, information on splicing and sequence heterogeneity is lost. To overcome this limitation, several approaches that use long-read sequencing were introduced recently. Yet, those techniques are limited by low sequencing depth and/or lacking or inaccurate assignment of unique molecular identifiers (UMIs), which are critical for elimination of PCR bias and artifacts. We introduce ScNaUmi-seq, an approach that combines the high throughput of Oxford Nanopore sequencing with an accurate cell barcode and UMI assignment strategy. UMI guided error correction allows to generate high accuracy full length sequence information with the 10x Genomics single cell isolation system at high sequencing depths. We analyzed transcript isoform diversity in embryonic mouse brain and show that ScNaUmi-seq allows defining splicing and SNVs (RNA editing) at a single cell level.

Detection of genome-wide low-frequency mutations with Paired-End and Complementary Consensus Sequencing (PECC-Seq) revealed end-repair-derived artifacts as residual errors.

To improve the accuracy and the cost-efficiency of next-generation sequencing in ultralow-frequency mutation detection, we developed the Paired-End and Complementary Consensus Sequencing (PECC-Seq), a PCR-free duplex consensus sequencing approach. PECC-Seq employed shear points as endogenous barcodes to identify consensus sequences from the overlap in the shortened, complementary DNA strand-derived paired-end reads for sequencing error correction. With the high accuracy of PECC-Seq, we identified the characteristic base substitution errors introduced by the end-repair process of mechanical fragmentation-based library preparations, which were prominent at the terminal 7bp of the library fragments in the 5'-NpCpA-3' and 5'-NpCpT-3' trinucleotide context. As demonstrated at the human genome scale (TK6 cells), after removing these potential end-repair artifacts from the terminal 7bp, PECC-Seq could reduce the sequencing error frequency to mid-10-7 with a relatively low sequencing depth. For TA base pairs, the background error rate could be suppressed to mid-10-8. In mutagen-treated (6μg/mL methyl methanesulfonate or 12μg/mL N-nitroso-N-ethylurea) TK6, increases in mutagen treatment-related mutant frequencies could be detected, indicating the potential of PECC-Seq in detecting genome-wide ultra-rare mutations. In addition, our finding on the patterns of end-repair artifacts may provide new insights into further reducing technical errors not only for PECC-Seq, but also for other next-generation sequencing techniques.

Human facial shape related SNP analysis in Han Chinese populations

Human facial morphology is one of the important visible biological characteristics. Understanding the genetic basis underlying facial shape traits has important implications in population genetics, developmental biology, and forensic science. This study extracted 136 Euclidean distance phenotypes from 17 facial features of high-resolution 3D facial images in 1177 Chinese Han adult males. Based on 3× low-depth sequencing data, linear regression was used to analyze the correlation between 125 reported SNPs significantly associated with facial morphology and 136 facial phenotypes. As a result, a total of twelve SNPs from ten genes demonstrated significant association with one or more facial shape traits after adjusting for multiple testing (significance threshold P < 1.35 × 10 -3 ), together explaining up to 3.89% of age-, and BMI-adjusted facial phenotype variance. These included TEX41 rs17479393, PAX3 rs974448, RAB7A/ACAD9 rs2977562, DCHS2 rs9995821, DCHS2 rs2045323, C5orf50 rs6555969, SUPT3H/RUNX2 rs1852985, MSRA rs11782517, EYA1 rs10504499, GSC rs2224309, DICER1 rs7161418 and DHX35 rs2206437.These results revealed the genetics basis of facial morphology of Han Chinese population, and provided reference data for DNA-based face prediction.

Comparison of Multiple Displacement Amplification (MDA) and Multiple Annealing and Looping-Based Amplification Cycles (MALBAC) in Limited DNA Sequencing Based on Tube and Droplet.

Whole genome amplification (WGA) is crucial for whole genome sequencing to investigate complex genomic alteration at the single-cell or even single-molecule level. Multiple displacement amplification (MDA) and multiple annealing and looping based amplification cycles (MALBAC) are two most widely applied WGA methods, which have different advantages and disadvantages, dependent on research objectives. Herein, we compared the MDA and MALBAC to provide more information on their performance in droplets and tubes. We observed that the droplet method could dramatically reduce the amplification bias and retain the high accuracy of replication than the conventional tube method. Furthermore, the droplet method exhibited higher efficiency and sensitivity for both homozygous and heterozygous single nucleotide variants (SNVs) at the low sequencing depth. In addition, we also found that MALBAC offered a greater uniformity and reproducibility and MDA showed a better efficiency of genomic coverage and SNV detection. Our results provided insights that will allow future decision making.

Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers.

The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group “exons” that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.