genome-guided mrna sequencing analysis tools  (BioCloud Inc)

Journal: Genome Biology

Article Title: LUMPY: a probabilistic framework for structural variant discovery

doi: 10.1186/gb-2014-15-6-r84

Figure Lengend Snippet: Receiver operating characteristic ( ROC) curves comparing deletion prediction performance in the NA12878 individual. The relationship between true positive and false positive calls for deletions in the NA12878 genome is given for LUMPY, GASVPro, DELLY, and Pindel. Each point on a given tool’s ROC curve represents a minimum evidence support threshold ranging from 4 to 11 for 5X coverage and 4 to 20 for 50X coverage. Correctness was determined by two different methods: intersection with one of the 3,376 non-overlapping validated deletions from Mills et al . , or validation by PacBio/Moleculo data. (A,B) As in Figure , prediction performance was measured with both 5X mean genome coverage (A) and 50X coverage (B) . The curves are colored following the same convention described in Figure . LUMPY outperforms all other tools in all but one case. Pindel slightly outperforms LUMPY at higher-evidence thresholds in the 5X coverage case considering the Mills et al . truth set; we note that this is expected given Pindel was used by the 1000 Genomes Project as one of the tools to define this truth set. At the lower coverage, LUMPY’s performance is boosted by the inclusion of either prior evidence or NA12878’s parental genomes, but the read-depth signal is too weak to offer any improvement. The distinction between tools at 50X coverage is low, but it is expected given the coverage and quality of the data. At higher coverage, LUMPY is able to provide a high-confidence call set when considering read-depth, but priors and parental genomes have little added benefit. pe, paired-end; rd, read-depth; sr, split-read.

Article Snippet: To estimate sensitivity and FDR, we compared predictions made by each tool to two truth sets: 1) 3,376 validated, non-overlapping deletions from the 1000 Genomes Project [ ] (Additional file ); and 2) 4,095 deletions that were detected by at least one tool in the 50X dataset, or that were reported by Mills et al . [ ] (which used numerous SV detection tools), and that were validated by split-read mapping analysis of independent long-read sequencing data from PacBio or Illumina Moleculo platforms (Additional file ).

Techniques:

Journal: Genome Biology

Article Title: LUMPY: a probabilistic framework for structural variant discovery

doi: 10.1186/gb-2014-15-6-r84

Figure Lengend Snippet: Performance comparison of deletion detection in high and low coverage Illumina sequencing data from NA12878. We analyzed an approximately 50X coverage dataset of the NA12878 genome from the Illumina Platinum Genomes dataset. We tested LUMPY’s performance under four different variant calling scenarios. First, ‘LUMPY (pe + sr)’ considered both paired-end (pe) and split-read (sr) alignments (using YAHA) from NA12878. Second, ‘LUMPY with prior’ considered pe and sr alignments as well as 1000 Genomes variants as prior evidence. Third, ‘LUMPY trio’ considered pe and sr alignments for NA12878 as well as alignments from her parents (NA12891 and NA12892). Lastly, ‘LUMPY with CNVnator’ integrated pe and sr alignments with copy number loss predictions made by CNVnator (read depth (rd)). DELLY considered pe and sr alignments, GASVPro considered pe alignments and rd, and Pindel considered sr alignments. Sensitivity and FDR were estimated using two truth sets: 3,376 non-overlapping validated deletions from Mills et al . , and 4,095 deletions that were predicted by at least one tool and validated by PacBio or Moleculo alignments. (A) SV detection sensitivity and FDR on a 5X coverage subsample of the original data. LUMPY pe + sr was more sensitive than both GASVPro and Pindel and had either an equivalent or better FDR. DELLY was more sensitive than LUMPY pe + sr, but also had a higher FDR. Prior evidence or parental genomes improved LUMPY sensitivity. Given the low coverage, the read-depth signal was weak and only a small number of CNVs clustered with paired-end or split-read calls. (B) SV detection sensitivity and FDR on the original 50X coverage data. LUMPY pe + sr, DELLY, and Pindel had similar sensitivity in the Mills et al . truth set, and in the PacBio/Moleculo truth set DELLY had the highest sensitivity and FDR. LUMPY pe + sr had the next best sensitivity and the lowest FDR.

Article Snippet: To estimate sensitivity and FDR, we compared predictions made by each tool to two truth sets: 1) 3,376 validated, non-overlapping deletions from the 1000 Genomes Project [ ] (Additional file ); and 2) 4,095 deletions that were detected by at least one tool in the 50X dataset, or that were reported by Mills et al . [ ] (which used numerous SV detection tools), and that were validated by split-read mapping analysis of independent long-read sequencing data from PacBio or Illumina Moleculo platforms (Additional file ).

Techniques: Sequencing, Variant Assay

Journal: Genome Biology

Article Title: LUMPY: a probabilistic framework for structural variant discovery

doi: 10.1186/gb-2014-15-6-r84

Figure Lengend Snippet: Detection performance in the NA12878 individual when restricting false discovery rates. We compared the performance of each tool in terms of sensitivity and novel variant discovery ability when considering only the subset of calls that meet a maximum FDR threshold. Using the results given in Figure , each tool’s FDR was calculated for each of the minimum-evidence settings used to generate the respective receiver operating characteristic (ROC) curves. This provided a mapping from the maximum FDR to the subset of calls that meet the associated minimum-evidence threshold for each tool. Sensitivity and FDR were estimated using the 4,095 deletions that were predicted by at least one tool and validated by PacBio or Moleculo alignments. (A) Sensitivity given a maximum FDR threshold. At 5X coverage, an FDR threshold of approximately 10% is achieved with a minimum of four alignments for LUMPY (8.1% FDR), four for GASVPro (10.1% FDR), six for DELLY (11.3% FDR), and nine for Pindel (6.3% FDR). An approximately 20% FDR at 50X coverage requires 8 alignments for LUMPY (18% FDR), 16 for GASVPro (19% FDR), 12 for DELLY (17.6% FDR), and 20 for Pindel (18.8% FDR). LUMPY had the highest sensitivity at both coverage levels and the relative improvement was most substantial at lower coverage. (B) Venn diagrams reflecting the absolute number of variants discovered uniquely and jointly among the different tools at both 10% FDR for 5X and 20% FDR for 50X. In both cases LUMPY found the most number of unique variants. The difference was most dramatic in the 5X coverage experiment, where only 46 out of 665 (6.9%) of the variants found among all four tools were missed by LUMPY. pe, paired-end; rd, read-depth; sr, split-read.

Article Snippet: To estimate sensitivity and FDR, we compared predictions made by each tool to two truth sets: 1) 3,376 validated, non-overlapping deletions from the 1000 Genomes Project [ ] (Additional file ); and 2) 4,095 deletions that were detected by at least one tool in the 50X dataset, or that were reported by Mills et al . [ ] (which used numerous SV detection tools), and that were validated by split-read mapping analysis of independent long-read sequencing data from PacBio or Illumina Moleculo platforms (Additional file ).

Techniques: Variant Assay

Journal: Cytotherapy

Article Title: Transcriptional dynamics of induced pluripotent stem cell differentiation into β cells reveals full endodermal commitment and homology with human islets.

doi: 10.1016/j.jcyt.2020.10.004

Figure Lengend Snippet: Figure 6. Pseudotime trajectories of ib cells. (A) Pseudotime trajectory ordering cells at the last stage of differentiation. Branch tips are nominated from A to E. (B) Pseudotime kinet- ics of INS gene. Cells are colored by marker expression level (from black to yellow). (C) Functional enrichment analyses of the most significant genes used to construct single-cell tra- jectory using STRING 11.0. (D) Interaction maps of the most significant genes leading pseudotime progression. Red dots indicate the DEGs for each tip. DEGs, differentially expressed genes.

Article Snippet: Single-cell RNA sequencing data analysis Raw reads were processed using Macosko-Nemesh [30] (https:// github.com/broadinstitute/Drop-seq/releases) and UMI-tools (https://github.com/CGATOxford/UMI-tools) pipelines.

Techniques: Marker, Expressing, Functional Assay, Construct