Journal: Genomics, Proteomics & Bioinformatics

Article Title: The Bioinformatic Applications of Hi-C and Linked Reads

doi: 10.1093/gpbjnl/qzae048

Figure Lengend Snippet: Work flow of library preparation for five long-range platforms A . Hi-C. B . 10X Genomics Linked Reads. C . Haplotagging, TELL-seq, and stLFR. Hi-C, high-throughput chromosome conformation capture; TELL-seq, transposase enzyme linked long-read sequencing; stLFR, single-tube long fragment read; GEM, gel bead in emulsion; HMW-DNA, high molecular weight DNA.

Article Snippet: In recent years, 10X Genomics Linked Reads (henceforth simply as “10X”) technology has been widely used, but a variety of other barcode-based methods, such as TruSeq, single-tube long fragment read (stLFR), transposase enzyme linked long-read sequencing (TELL-seq), LoopSeq, and haplotagging, have been developed for ultralow DNA input, high per-base resolution, and low costs [ ].

Techniques: Hi-C, High Throughput Screening Assay, Sequencing, Emulsion, High Molecular Weight

Journal: Genomics, Proteomics & Bioinformatics

Article Title: The Bioinformatic Applications of Hi-C and Linked Reads

doi: 10.1093/gpbjnl/qzae048

Figure Lengend Snippet: Evenness statistics

Article Snippet: In recent years, 10X Genomics Linked Reads (henceforth simply as “10X”) technology has been widely used, but a variety of other barcode-based methods, such as TruSeq, single-tube long fragment read (stLFR), transposase enzyme linked long-read sequencing (TELL-seq), LoopSeq, and haplotagging, have been developed for ultralow DNA input, high per-base resolution, and low costs [ ].

Techniques:

Journal: Genomics, Proteomics & Bioinformatics

Article Title: The Bioinformatic Applications of Hi-C and Linked Reads

doi: 10.1093/gpbjnl/qzae048

Figure Lengend Snippet: Length distributions for various 10X and haplotagging datasets Reads are grouped into fragments by barcodes, with shared barcodes identified and removed. The length of a fragment is the region covered by mapping coordinates from the Linked Reads which share the same barcode.

Article Snippet: In recent years, 10X Genomics Linked Reads (henceforth simply as “10X”) technology has been widely used, but a variety of other barcode-based methods, such as TruSeq, single-tube long fragment read (stLFR), transposase enzyme linked long-read sequencing (TELL-seq), LoopSeq, and haplotagging, have been developed for ultralow DNA input, high per-base resolution, and low costs [ ].

Techniques:

Journal: Genomics, Proteomics & Bioinformatics

Article Title: The Bioinformatic Applications of Hi-C and Linked Reads

doi: 10.1093/gpbjnl/qzae048

Figure Lengend Snippet: Features of 10X, haplotagging, and stLFR datasets

Article Snippet: In recent years, 10X Genomics Linked Reads (henceforth simply as “10X”) technology has been widely used, but a variety of other barcode-based methods, such as TruSeq, single-tube long fragment read (stLFR), transposase enzyme linked long-read sequencing (TELL-seq), LoopSeq, and haplotagging, have been developed for ultralow DNA input, high per-base resolution, and low costs [ ].

Techniques:

Journal: Genomics, Proteomics & Bioinformatics

Article Title: The Bioinformatic Applications of Hi-C and Linked Reads

doi: 10.1093/gpbjnl/qzae048

Figure Lengend Snippet: Base coverage profiles for various 10X and haplotagging datasets The 10X and haplotagging downsampled datasets at ∼ 30× are used to remove the effects of differing coverage depths. In terms of coverage evenness, the datasets of rat and oak are not as smooth as other samples.

Article Snippet: In recent years, 10X Genomics Linked Reads (henceforth simply as “10X”) technology has been widely used, but a variety of other barcode-based methods, such as TruSeq, single-tube long fragment read (stLFR), transposase enzyme linked long-read sequencing (TELL-seq), LoopSeq, and haplotagging, have been developed for ultralow DNA input, high per-base resolution, and low costs [ ].

Techniques:

Journal: Genomics, Proteomics & Bioinformatics

Article Title: The Bioinformatic Applications of Hi-C and Linked Reads

doi: 10.1093/gpbjnl/qzae048

Figure Lengend Snippet: Analysis tools for long-range reads

Article Snippet: In recent years, 10X Genomics Linked Reads (henceforth simply as “10X”) technology has been widely used, but a variety of other barcode-based methods, such as TruSeq, single-tube long fragment read (stLFR), transposase enzyme linked long-read sequencing (TELL-seq), LoopSeq, and haplotagging, have been developed for ultralow DNA input, high per-base resolution, and low costs [ ].

Techniques: Software, Scaffolding

Journal: Nature Communications

Article Title: A chromosome-scale assembly of the sorghum genome using nanopore sequencing and optical mapping

doi: 10.1038/s41467-018-07271-1

Figure Lengend Snippet: MUMmerplot comparison of Tx430 ONT assembly with the BTx623 reference assembly. ONT contigs ( Y -axis) ( Tx430 ) were aligned to all 10 chromosomes from the public BTx623 v3.0.1 genome assembly ( X -axis) ( BTx623) with NUCmer, and results were subsequently filtered for 1-on-1 alignments and rearrangements with a 20 Kbps length cutoff. BTx623 chromosomes are labeled by number and contain multiple megabase-scale regions in the form of unresolved nucleotide sequences

Article Snippet: Finally, the Tx430 10X Genomics reads were assembled de novo with the Supernova assembler into 49,789 contigs, totaling ~613 Mbps.

Techniques: Comparison, Labeling

Journal: Nature Communications

Article Title: A chromosome-scale assembly of the sorghum genome using nanopore sequencing and optical mapping

doi: 10.1038/s41467-018-07271-1

Figure Lengend Snippet: Alignment of Tx430 maps against in-silico maps of the BTx623 reference assembly. Collinear DLE-1 markers on the two maps are linked (gray lines). All but three chromosomes are captured by two DLS maps. Large inversions are shown on chromosomes 6 and 7. Regions in green are stretches of random nucleotides in the reference assembly. Regions in yellow exhibit breaks in collinearity between the two maps

Article Snippet: Finally, the Tx430 10X Genomics reads were assembled de novo with the Supernova assembler into 49,789 contigs, totaling ~613 Mbps.

Techniques: In Silico

Journal: Nature Communications

Article Title: A chromosome-scale assembly of the sorghum genome using nanopore sequencing and optical mapping

doi: 10.1038/s41467-018-07271-1

Figure Lengend Snippet: MUMmerplot comparison of Tx430 hybrid scaffolds with the reference BTx623 assembly. Hybrid scaffolds ( Y -axis) were aligned to all 10 BTx623 chromosomes ( X -axis) using NUCmer and alignments were subsequently filtered for 1-on-1 alignments and rearrangements with a 20 Kbps length cutoff. Chromosome order on the X -axis is related to the alignment of Tx430 scaffolds mapping to more than one chromosome. Chromosomal inversions and breakage in scaffold orientation in relation to the chromosomal sequence on the X -axis are shown as blue lines

Article Snippet: Finally, the Tx430 10X Genomics reads were assembled de novo with the Supernova assembler into 49,789 contigs, totaling ~613 Mbps.

Techniques: Comparison, Sequencing

Journal: Nature Communications

Article Title: A chromosome-scale assembly of the sorghum genome using nanopore sequencing and optical mapping

doi: 10.1038/s41467-018-07271-1

Figure Lengend Snippet: Close-up view of one inversion breakpoint area on Chromosome 6. A 50 Kbps region is shown, where Tx430 Chromium 10X linked reads, Tx430 Illumina whole genome shotgun (WGS) reads, individual Tx430 ONT reads aligning to the region and RepeatMasker screening output are shown, from top to bottom. The approximate location of the breakpoint area is marked by an arrow

Article Snippet: Finally, the Tx430 10X Genomics reads were assembled de novo with the Supernova assembler into 49,789 contigs, totaling ~613 Mbps.

Techniques:

Journal: Nature Communications

Article Title: A chromosome-scale assembly of the sorghum genome using nanopore sequencing and optical mapping

doi: 10.1038/s41467-018-07271-1

Figure Lengend Snippet: Annotation comparison metrics

Article Snippet: Finally, the Tx430 10X Genomics reads were assembled de novo with the Supernova assembler into 49,789 contigs, totaling ~613 Mbps.

Techniques: Comparison

Journal: Scientific Reports

Article Title: Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines

doi: 10.1038/s41598-018-23342-1

Figure Lengend Snippet: Summary of the 10x GemCode sequencing data and phasing.

Article Snippet: To further characterize these potential regulatory SNVs, we first attempted to associate regulatory SNVs with their transcribed regions based on the “phasing.” For this purpose, we applied the synthetic long read sequencing technology (10x Genomics GemCode) to the lung adenocarcinoma cell lines (Fig. ).

Techniques: Sequencing, Blocking Assay

Journal: Scientific Reports

Article Title: Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines

doi: 10.1038/s41598-018-23342-1

Figure Lengend Snippet: Phasing regulatory SNVs to the transcripts. ( A ) The distribution of phase blocks by length. The results were biased toward multiple, smaller blocks. ( B ) The distribution of phase blocks by number of member SNVs. The trends followed the length distribution in ( A ). ( C ) The distribution of phase blocks by reported haplotypes. ( D ) The distribution of phase blocks with regulatory SNVs by number of member SNVs. The distribution of the block length is shown in the inset. Blocks with regulatory SNVs showed no special characteristics or bias. ( E ) An example of the association between regulatory SNVs and its target transcript of the CYP1B1 gene found in A549 cell line. Haplotypes were separated by black lines, and blue curved lines represent direct connection via linked read/MIs. ( F ) Validation of the phasing result by physical long reads sequenced from ONT MinION sequencing. Summary of the validation analysis is shown in the upper table for H1975, RERF-LC-KJ and LC2/ad cell lines. The lower panel shows one particular phase block in the H1975 cell line covering the SEMA6A gene region which was confirmed by both synthetic long read and physical long read phasing. Phasing from synthetic long reads is shown by thin black lines and that from physical long reads is shown by colored thick lines. ( G ) An example of TADs with regulatory mutations. A regulatory mutation within the same TAD of the TSS were visualized in A549 HiC data using the WashU EpiGenome Browser.

Article Snippet: To further characterize these potential regulatory SNVs, we first attempted to associate regulatory SNVs with their transcribed regions based on the “phasing.” For this purpose, we applied the synthetic long read sequencing technology (10x Genomics GemCode) to the lung adenocarcinoma cell lines (Fig. ).

Techniques: Blocking Assay, Biomarker Discovery, Sequencing, Mutagenesis

Journal: Scientific Reports

Article Title: Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines

doi: 10.1038/s41598-018-23342-1

Figure Lengend Snippet: Allelic imbalance of transcriptional regulations. ( A ) Imbalance in SNP/SNV allelic expression (>5-fold changes, P < 0.01) detected in RNA sequencing. Red dots represent chromosome X imbalances. Dark blue dots represent other chromosome imbalances and light blue dots represent balanced expression. ( B ) Number of transcripts with imbalanced expression. Red bars represent chromosome X and blue bars represent other chromosomes. ( C ) Allelic ChIP imbalance of regulatory SNVs. Red dots represent chromosome X imbalances. Dark blue dots represent other chromosome imbalance and light blue dots represent balanced expression. The lower table shows the number of SNPs/SNVs in each category. ( D ) Histogram and line graph showing the number of imbalanced (both RNA and ChIP), phased RefSeq transcripts (gray line) and regulatory SNVs potentially influencing these transcripts (chromosome X in red, others in blue). ( E ) An example of regulatory mutations potentially causing biased transcript allele expression ( ZDBF2 in H2126). Pol-II and H3K9/14ac markers located in intron 1 showed bias in the ChIP-Seq and coding SNPs/SNVs in exon 5 showed expression bias in the transcriptomes.

Article Snippet: To further characterize these potential regulatory SNVs, we first attempted to associate regulatory SNVs with their transcribed regions based on the “phasing.” For this purpose, we applied the synthetic long read sequencing technology (10x Genomics GemCode) to the lung adenocarcinoma cell lines (Fig. ).

Techniques: Expressing, RNA Sequencing, ChIP-sequencing

Journal: Scientific Reports

Article Title: Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines

doi: 10.1038/s41598-018-23342-1

Figure Lengend Snippet: Regulatory mutations and aberrant transcription in lung cancer cell lines. ( A ) Allelic transcriptional and copy number bias of PDGFRA gene in H1703 cell line. Three regulatory mutations (green) and five coding SNPs (bright green) were phased. The number of sequencing tags in each haplotype were shown in small tables (upper: whole-genome sequencing; lower: ChIP-Seq or RNA-Seq). ( B ) Patterns of the regulatory mutations. The number of regulatory mutations were shown in the upper panel. Background mutational signatures are shown in the middle panel. Three background signatures (Smoking, aging and APOBEC signatures) were defined using somatic SNVs in coding regions. The lower panel shows known driver mutation statuses ( EGFR , KRAS and NRAS mutations) for each cell line with color legends in the right margin. ( C ) Sequence contexts of regulatory mutations. Regulatory SNVs overlapping with the CpG sites, ENCODE ChIP-Seq peaks and TRANSFAC transcription factor binding sites were shown in the upper, middle and lower panels, respectively. ( D , E ) Examples of the regulatory SNVs in SLC16A4 of H1650 cell line ( D ) and NFATC1 of RERF-LC-Ad1 cell line ( E ). The regulatory mutation (green) and coding SNPs (bright green) were represented with the IGV (left). The changes of potential transcription factor binding sites are shown in the right panel. ( F ) The target region of the NFATC1 upstream regulatory region. The blue arrow shows DNA fragment used for luciferase assays. Primers for ChIP-qPCR are shown as black arrows. Both the DNA fragment and primers covered the NFATC1 regulatory SNV. ( G ) Luciferase assays for the NFATC1 regulatory SNV. Relative activities of the NFATC1 regulatory region were compared between wildtype (WT) and mutant sequences. Results were averaged from 3 biological replicates with 2 technical replicates each (n = 6) ( H ) ChIP-qPCR of ETS1 in RERF-LC-Ad1 cell line (one primer for positive control RPS26 and three primers for the NFATC1 regulatory SNV as targets). Fold enrichments of ChIP DNA are shown in the graph. Results were averaged from 3 biological replicates with 2 technical replicates each (n = 6). See Fig. 4F for target regions of the NFATC1 regulatory region and Supplementary Table for primer sequences. ( I ) Direct Sanger sequencing of the NFATC1 regulatory region in ETS1 ChIP samples. Chromatogram of Sanger sequencing were shown in left (upper: ChIP DNA, lower: input DNA). The consensus sequence of ETS1 binding sites is shown over the chromatogram. Fold enrichment of the mutant allele (“G”) compared with wildtype allele (“C”) is shown in the margin. ( J ) Kaplan-Meier analysis of cases in TCGA-LUAD data divided into two groups depending on the expression level of NFATC1 . Overall survival and disease-free survival were shown in the left and right panels, respectively.

Article Snippet: To further characterize these potential regulatory SNVs, we first attempted to associate regulatory SNVs with their transcribed regions based on the “phasing.” For this purpose, we applied the synthetic long read sequencing technology (10x Genomics GemCode) to the lung adenocarcinoma cell lines (Fig. ).

Techniques: Sequencing, ChIP-sequencing, RNA Sequencing, Mutagenesis, Binding Assay, Luciferase, ChIP-qPCR, Positive Control, Expressing