Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth.

BACKGROUND: The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequencing (LP-WGS) has been reported for the detection of AOH in a low-pass setting of less than onefold. We developed a method, termed CNVseq-AOH, for predicting the absence of heterozygosity using LP-WGS with ultra-low sequencing data, which overcomes the sparse nature of typical LP-WGS data by combing population-based haplotype information, adjustable sliding windows, and recurrent neural network (RNN). We tested the feasibility of CNVseq-AOH for the detection of AOH in 409 cases (11 AOH regions for model training and 863 AOH regions for validation) from the 1000 Genomes Project (1KGP). AOH detection using CNVseq-AOH was also performed on 6 clinical cases with previously ascertained AOHs by whole exome sequencing (WES). RESULTS: Using SNP-based microarray results as reference (AOHs detected by CNVseq-AOH with at least a 50% overlap with the AOHs detected by chromosomal microarray analysis), 409 samples (863 AOH regions) in the 1KGP were used for concordant analysis. For 784 AOHs on autosomes and 79 AOHs on the X chromosome, CNVseq-AOH can predict AOHs with a concordant rate of 96.23% and 59.49% respectively based on the analysis of 0.1-fold LP-WGS data, which is far lower than the current standard in the field. Using 0.1-fold LP-WGS data, CNVseq-AOH revealed 5 additional AOHs (larger than 10 Mb in size) in the 409 samples. We further analyzed AOHs larger than 10 Mb, which is recommended for reporting the possibility of UPD. For the 291 AOH regions larger than 10 Mb, CNVseq-AOH can predict AOHs with a concordant rate of 99.66% with only 0.1-fold LP-WGS data. In the 6 clinical cases, CNVseq-AOH revealed all 15 known AOH regions. CONCLUSIONS: Here we reported a method for analyzing LP-WGS data to accurately identify regions of AOH, which possesses great potential to improve genetic testing of AOH.

Accuracy and depth evaluation of clinical low pass genome sequencing in the detection of mosaic aneuploidies and CNVs.

BACKGROUND: Low-pass genome sequencing (LP GS) has shown distinct advantages over traditional methods for the detection of mosaicism. However, no study has systematically evaluated the accuracy of LP GS in the detection of mosaic aneuploidies and copy number variants (CNVs) in prenatal diagnosis. Moreover, the influence of sequencing depth on mosaicism detection of LP GS has not been fully evaluated. METHODS: To evaluate the accuracy of LP GS in the detection of mosaic aneuploidies and mosaic CNVs, 27 samples with known aneuploidies and CNVs and 1 negative female sample were used to generate 6 simulated samples and 21 virtual samples, each sample contained 9 different mosaic levels. Mosaic levels were simulated by pooling reads or DNA from each positive sample and the negative sample according to a series of percentages (ranging from 3 to 40%). Then, the influence of sequencing depth on LP GS in the detection of mosaic aneuploidies and CNVs was evaluated by downsampling. RESULTS: To evaluate the accuracy of LP GS in the detection of mosaic aneuploidies and CNVs, a comparative analysis of mosaic levels was performed using 6 simulated samples and 21 virtual samples with 35 M million (M) uniquely aligned high-quality reads (UAHRs). For mosaic levels > 30%, the average difference (detected mosaic levels vs. theoretical mosaic levels) of 6 mosaic CNVs in simulated samples was 4.0%, and the average difference (detected mosaic levels vs. mosaic levels of Y chromosome) of 6 mosaic aneuploidies and 15 mosaic CNVs in virtual samples was 2.7%. Furthermore, LP GS had a higher detection rate and accuracy for the detection of mosaic aneuploidies and CNVs of larger sizes, especially mosaic aneuploidies. For depth evaluation, the results of LP GS in downsampling samples were compared with those of LP GS using 35 M UAHRs. The detection sensitivity of LP GS for 6 mosaic aneuploidies and 15 mosaic CNVs in virtual samples increased with UAHR. For mosaic levels > 30%, the total detection sensitivity reached a plateau at 30 M UAHRs. With 30 M UAHRs, the total detection sensitivity was 99.2% for virtual samples. CONCLUSIONS: We demonstrated the accuracy of LP GS in mosaicism detection using simulated data and virtual samples, respectively. Thirty M UAHRs (single-end 35 bp) were optimal for LP GS in the detection of mosaic aneuploidies and most mosaic CNVs larger than 1.48 Mb (Megabases) with mosaic levels > 30%. These results could provide a reference for laboratories that perform clinical LP GS in the detection of mosaic aneuploidies and CNVs.

Clinical diagnosis of genetic disorders at both single-nucleotide and chromosomal levels based on BGISEQ-500 platform.

Most variations in the human genome refer to single-nucleotide variation (SNV), small fragment insertions and deletions, and genomic copy number variation (CNV). Many human diseases including genetic disorders are associated with variations in the genome. These disorders are often difficult to be diagnosed because of their complex clinical conditions, therefore, an effective detection method is needed to facilitate clinical diagnosis and prevent birth defects. With the development of high-throughput sequencing technology, the method of targeted sequence capture chip has been extensively used owing to its high throughput, high accuracy, fast speed, and low cost. In this study, we designed a chip that potentially captured the coding region of 3043 genes associated with 4013 monogenic diseases, with an addition of 148 chromosomal abnormalities that can be identified by targeting specific regions. To assess the efficiency, a strategy of combining the BGISEQ500 sequencing platform with the designed chip was utilized to screen variants in 63 patients. Eventually, 67 disease-associated variants were found, 31 of which were novel. The results of the evaluation test also show that this combined strategy complies with the requirements of clinical testing and has proper clinical application value.

Validation and depth evaluation of low-pass genome sequencing in prenatal diagnosis using 387 amniotic fluid samples.

BACKGROUND: Low-pass genome sequencing (LP GS) is an alternative to chromosomal microarray analysis (CMA). However, validations of LP GS as a prenatal diagnostic test for amniotic fluid are rare. Moreover, sequencing depth of LP GS in prenatal diagnosis has not been evaluated. OBJECTIVE: The diagnostic performance of LP GS was compared with CMA using 375 amniotic fluid samples. Then, sequencing depth was evaluated by downsampling. RESULTS: CMA and LP GS had the same diagnostic yield (8.3%, 31/375). LP GS showed all copy number variations (CNVs) detected by CMA and six additional variant of uncertain significance CNVs (>100 kb) in samples with negative CMA results; CNV size influenced LP GS detection sensitivity. CNV detection was greatly influenced by sequencing depth when the CNV size was small or the CNV was located in the azoospermia factor c (AZFc) region of the Y chromosome. Large CNVs were less affected by sequencing depth and more stably detected. There were 155 CNVs detected by LP GS with at least a 50% reciprocal overlap with CNVs detected by CMA. With 25 M uniquely aligned high-quality reads (UAHRs), the detection sensitivity for the 155 CNVs was 99.14%. LP GS using samples with 25 M UAHRs showed the same performance as LP GS using total UAHRs. Considering the detection sensitivity, cost and interpretation workload, 25 M UAHRs are optimal for detecting most aneuploidies and microdeletions/microduplications. CONCLUSION: LP GS is a promising, robust alternative to CMA in clinical settings. A total of 25 M UAHRs are sufficient for detecting aneuploidies and most microdeletions/microduplications.

Panel-based NGS reveals disease-causing mutations in hearing loss patients using BGISEQ-500 platform.

Hearing loss is a highly heterogeneous disease presented with various phenotypes. Genetic testing of disease-causing mutations plays an important role in precise diagnosis and fertility guidance of heredity hearing loss. Here we reported an effective method employing target enrichment and BGISEQ-500 platform to detect clinically relevant alterations for heredity hearing patients in a single assay.In this study, we designed an array based chip, containing 127 genes related to hearing loss. Then we conducted targeted next-generation sequencing toward 58 patients to make a precise diagnosis using BGISEQ-500 platform.We successfully detected disease-causing mutations in 77.59% (45/58) of the patients with hearing loss. Finally, a total of 62 disease-causing mutations were identified, including 31 missense, 17 Indel, 11 splicing, 2 synonymous, and 1 copy number variant. 58.06% (36/62) of which has never been reported before.To our knowledge, this is the first report using BGISEQ-500 platform to investigate both syndromic and nonsyndromic hearing loss in the Chinese population. The results showed that this method can greatly assist and enhance hearing loss diagnosis and improve molecular diagnostics outcome.

Targeted next-generation sequencing as a comprehensive test for Mendelian diseases: a cohort diagnostic study.

With the development of next generation sequencing, more and more common inherited diseases have been reported. However, accurate and convenient molecular diagnosis cannot be achieved easily because of the enormous size of disease causing mutations. In this study, we introduced a new single-step method for the genetic analysis of patients and carriers in real clinical settings. All kinds of disease causing mutations can be detected at the same time in patients with Mendelian diseases or carriers. First, we evaluated this technology using YH cell line DNA and 9 samples with known mutations. Accuracy and stability of 99.80% and 99.58% were achieved respectively. Then, a total of 303 patients were tested using our targeted NGS approaches, 50.17% of which were found to have deleterious mutations and molecular confirmation of the clinical diagnosis. We identified 219 disease causing mutations, 43.84% (96/219) of which has never been reported before. Additionally, we developed a new deleteriousness prediction method for nonsynonymous SNVs, and an automating annotation and diagnosis system for Mendelian diseases, thus greatly assisting and enhancing Mendelian diseases diagnosis and helping to make a precise diagnosis for patients with Mendelian diseases.

Correction: Targeted Next-Generation Sequencing for Clinical Diagnosis of 561 Mendelian Diseases.

[This corrects the article DOI: 10.1371/journal.pone.0133636.].

Targeted Next-Generation Sequencing for Clinical Diagnosis of 561 Mendelian Diseases.

BACKGROUND: Targeted next-generation sequencing (NGS) is a cost-effective approach for rapid and accurate detection of genetic mutations in patients with suspected genetic disorders, which can facilitate effective diagnosis. METHODOLOGY/PRINCIPAL FINDINGS: We designed a capture array to mainly capture all the coding sequence (CDS) of 2,181 genes associated with 561 Mendelian diseases and conducted NGS to detect mutations. The accuracy of NGS was 99.95%, which was obtained by comparing the genotypes of selected loci between our method and SNP Array in four samples from normal human adults. We also tested the stability of the method using a sample from normal human adults. The results showed that an average of 97.79% and 96.72% of single-nucleotide variants (SNVs) in the sample could be detected stably in a batch and different batches respectively. In addition, the method could detect various types of mutations. Some disease-causing mutations were detected in 69 clinical cases, including 62 SNVs, 14 insertions and deletions (Indels), 1 copy number variant (CNV), 1 microdeletion and 2 microduplications of chromosomes, of which 35 mutations were novel. Mutations were confirmed by Sanger sequencing or real-time polymerase chain reaction (PCR). CONCLUSIONS/SIGNIFICANCE: Results of the evaluation showed that targeted NGS enabled to detect disease-causing mutations with high accuracy, stability, speed and throughput. Thus, the technology can be used for the clinical diagnosis of 561 Mendelian diseases.

Automated and fast building of three-dimensional RNA structures.

Building tertiary structures of non-coding RNA is required to understand their functions and design new molecules. Current algorithms of RNA tertiary structure prediction give satisfactory accuracy only for small size and simple topology and many of them need manual manipulation. Here, we present an automated and fast program, 3dRNA, for RNA tertiary structure prediction with reasonable accuracy for RNAs of larger size and complex topology.