Journal: G3: Genes|Genomes|Genetics

Article Title: A long reads-based de-novo assembly of the genome of the Arlee homozygous line reveals chromosomal rearrangements in rainbow trout

doi: 10.1093/g3journal/jkab052

Figure Lengend Snippet: Statistical data summary of the USDA_OmykA_1.1 (Arlee) rainbow trout genome assembly

Article Snippet: The total length of the 710 unplaced scaffolds that are not anchored to a chromosome is ∼108 Mb. table ft1 table-wrap mode="anchored" t5 caption a7 Feature Canu contigs Polished contigs BioNano scaffolds Hi-C scaffolds With linkage information Number of sequences 1,591 1,591 1,044 919 938 Total length 2,340,653,759 2,341,478,269 2,341,947,072 2,342,042,072 2,341,652,372 Maximum length 63,126,076 63,163,333 88,429,459 90,526,592 88,429,459 Minimum length 1,061 1,057 16,956 16,956 16,956 N50 9,835,815 9,837,718 28,011,862 47,542,702 39,165,350 L50 58 58 31 17 22 N90 1,125,404 1,125,715 1,804,217 2,489,804 2,487,114 L90 333 333 170 98 116 BUSCO * C: 95.9% (S: 48.3%; D: 47.6%) C: 96.2% (S: 46.2%; D: 50.0%) NA C: 96.4% (S: 46.7%; D: 49.7%) NA Open in a separate window * Benchmarking Universal Single-Copy Orthologs.

Techniques:

Journal: The Plant Cell

Article Title: Is It Ordered Correctly? Validating Genome Assemblies by Optical Mapping ^[OPEN]

doi: 10.1105/tpc.17.00514

Figure Lengend Snippet: Recent Results of Physical Maps Aligned to Their Respective Reference Genomes

Article Snippet: Some genomic regions are easily corrected, others require multiple iterations to untangle and resolve discrepancies , and others will likely remain unresolved and may require local reassembly of the underlying DNA sequence. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 3. caption a7 Sequence Contigs from G. herbaceum Chromosome 4 Ordered and Oriented into Pseudomolecules by the Hi-C Methodology (as Assembled by PhaseGenomics). (A) The first row is a colored bar that represents the concatenated contigs based on clustering and orientation likelihood ratios of Hi-C data.

Techniques: Sequencing

Journal: The Plant Cell

Article Title: Is It Ordered Correctly? Validating Genome Assemblies by Optical Mapping ^[OPEN]

doi: 10.1105/tpc.17.00514

Figure Lengend Snippet: An Illustration of Bionano Contigs Likely Spanning Centromeric Regions in the G. herbaceum Reference.

Article Snippet: Some genomic regions are easily corrected, others require multiple iterations to untangle and resolve discrepancies , and others will likely remain unresolved and may require local reassembly of the underlying DNA sequence. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 3. caption a7 Sequence Contigs from G. herbaceum Chromosome 4 Ordered and Oriented into Pseudomolecules by the Hi-C Methodology (as Assembled by PhaseGenomics). (A) The first row is a colored bar that represents the concatenated contigs based on clustering and orientation likelihood ratios of Hi-C data.

Techniques:

Journal: The Plant Cell

Article Title: Is It Ordered Correctly? Validating Genome Assemblies by Optical Mapping ^[OPEN]

doi: 10.1105/tpc.17.00514

Figure Lengend Snippet: Sequence Contigs from G. herbaceum Chromosome 4 Ordered and Oriented into Pseudomolecules by the Hi-C Methodology (as Assembled by PhaseGenomics).

Article Snippet: Some genomic regions are easily corrected, others require multiple iterations to untangle and resolve discrepancies , and others will likely remain unresolved and may require local reassembly of the underlying DNA sequence. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 3. caption a7 Sequence Contigs from G. herbaceum Chromosome 4 Ordered and Oriented into Pseudomolecules by the Hi-C Methodology (as Assembled by PhaseGenomics). (A) The first row is a colored bar that represents the concatenated contigs based on clustering and orientation likelihood ratios of Hi-C data.

Techniques: Sequencing, Hi-C

Journal: Clinical Medicine

Article Title: The rise of the genome and personalised medicine

doi: 10.7861/clinmedicine.17-6-545

Figure Lengend Snippet: An overview of whole genome sequencing (WGS), whole exome sequencing (WES) and static gene panel techniques. Adapted from graphics provided by Genomics England.

Article Snippet: Adapted from graphics provided by Genomics England. table ft1 table-wrap mode="anchored" t5 Table 1. caption a7 Approach Definition Advantages Disadvantages Whole genome sequencing (WGS) Sequencing of the entire genome list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Comprehensive Even coverage enabling identification of dosage abnormalities/ structural rearrangements Detects non-coding variants Potential to detect disorders caused by DNA repeats, including Fragile X, myotonic dystrophy Able to detect mitochondrial mutations list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Expensive Millions of variants, which can be difficult to interpret Relatively shallow sequencing (fewer reads per gene), which can affect sensitivity; however, this is improving as technology advances Need a clear system to deal with additional findings Whole exome sequencing (WES) or clinical exome sequencing Sequencing of the exons (coding region) of genes or sequencing of the exons of known disease genes list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Cheaper than WGS Analysis not restricted to genes known to cause a given condition Fewer variants than WGS so easier to interpret Deep sequencing increases sensitivity Able to detect mitochondrial mutations list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Less even coverage of the genome, therefore dosage abnormalities are more difficult to detect Only 1–2% of the genome is covered Need a clear system to deal with additional findings Targeted gene sequencing via a static gene panel Sequencing of a specific list of pre-determined genes that are known to cause a particular phenotype list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Cost effective Very deep sequencing Fewer variants detected so data easier to interpret list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Difficult to add new genes to the panel as they are discovered as this requires a redesign of the capture process Less even coverage of the subset of genes, therefore dosage abnormalities are harder to detect Patients do not benefit from additional health findings Open in a separate window A comparison of whole genome sequencing, whole exome sequencing and static gene panel techniques The genome and rare disease There are an estimated 6,000 rare diseases, each defined as affecting less than 1 in 2,000 people.

Techniques: Sequencing

Journal: Clinical Medicine

Article Title: The rise of the genome and personalised medicine

doi: 10.7861/clinmedicine.17-6-545

Figure Lengend Snippet: A comparison of whole genome sequencing, whole exome sequencing and static gene panel techniques

Article Snippet: Adapted from graphics provided by Genomics England. table ft1 table-wrap mode="anchored" t5 Table 1. caption a7 Approach Definition Advantages Disadvantages Whole genome sequencing (WGS) Sequencing of the entire genome list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Comprehensive Even coverage enabling identification of dosage abnormalities/ structural rearrangements Detects non-coding variants Potential to detect disorders caused by DNA repeats, including Fragile X, myotonic dystrophy Able to detect mitochondrial mutations list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Expensive Millions of variants, which can be difficult to interpret Relatively shallow sequencing (fewer reads per gene), which can affect sensitivity; however, this is improving as technology advances Need a clear system to deal with additional findings Whole exome sequencing (WES) or clinical exome sequencing Sequencing of the exons (coding region) of genes or sequencing of the exons of known disease genes list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Cheaper than WGS Analysis not restricted to genes known to cause a given condition Fewer variants than WGS so easier to interpret Deep sequencing increases sensitivity Able to detect mitochondrial mutations list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Less even coverage of the genome, therefore dosage abnormalities are more difficult to detect Only 1–2% of the genome is covered Need a clear system to deal with additional findings Targeted gene sequencing via a static gene panel Sequencing of a specific list of pre-determined genes that are known to cause a particular phenotype list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Cost effective Very deep sequencing Fewer variants detected so data easier to interpret list-behavior=unordered prefix-word= mark-type=disc max-label-size=0 Difficult to add new genes to the panel as they are discovered as this requires a redesign of the capture process Less even coverage of the subset of genes, therefore dosage abnormalities are harder to detect Patients do not benefit from additional health findings Open in a separate window A comparison of whole genome sequencing, whole exome sequencing and static gene panel techniques The genome and rare disease There are an estimated 6,000 rare diseases, each defined as affecting less than 1 in 2,000 people.

Techniques: Comparison, Sequencing

Journal:

Article Title: Variations in the csgD Promoter of Escherichia coli O157:H7 Associated with Increased Virulence in Mice and Increased Invasion of HEp-2 Cells

doi: 10.1128/IAI.70.1.395-399.2002

Figure Lengend Snippet: Comparative Vero cell Stx assay for red (curliated) and white (noncurliated) variants of ATCC 43895 E. coli O157:H7. NC, negative control; PC, positive control.

Article Snippet: These results indicate that the in vitro invasion and in vivo mouse mortality differences between the red and white variants were not due to differential Stx production. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window FIG. 4. caption a7 Comparative Vero cell Stx assay for red (curliated) and white (noncurliated) variants of ATCC 43895 E. coli O157:H7.

Techniques: Negative Control, Positive Control

Journal: Transplantation

Article Title: Single Cell Transcriptomics and Solid Organ Transplantation

doi: 10.1097/TP.0000000000002725

Figure Lengend Snippet: A comparison of current high throughput droplet based single cell RNA-seq methods

Article Snippet: Fortunately, an increasing variety of programs are simplifying this process which lowers the bar of entry for laboratories without coding experience. table ft1 table-wrap mode="anchored" t5 Table 1. caption a7 DropSeq InDrops 10X Chromium Year developed 2015 2015 2016 Commercial platform No 1CellBio 10X Genomics Full length sequence No No No 3’ or 5’ sequence 3’ 3’ both Capture efficiency 12.8% 90% 65% Sensitivity (molecule detection limit) 1e+1 1e+0.5 1e+2.5 Accuracy (Pearson R) >0.9 >0.9 >0.9 Cell capacity per run 10,000 10,000 80,000 Open in a separate window A comparison of current high throughput droplet based single cell RNA-seq methods Although beyond the scope of this review, a variety of machine learning techniques allow for analysis and data visualization ( ).

Techniques: Comparison, High Throughput Screening Assay, Sequencing

Journal: Cancer cell

Article Title: Advancing Cancer Research and Medicine with Single Cell Genomics

doi: 10.1016/j.ccell.2020.03.008

Figure Lengend Snippet: Technologies for High-Throughput Single Cell Sequencing

Article Snippet: Overall, scRNA-seq approaches represent the most mature SCS methods, however they still have several technical limitations including high transcript drop-outs for low expressed genes, low total gene counts per cell and high bias for 3’ coverage. table ft1 table-wrap mode="anchored" t5 Table 1 - caption a7 Method Technology Name Platform Cost Chemistry References scRNA-seq microdroplets 10X Genomics RNA commercial 10K $$$$ 3' or 5' Zheng et al. 2017 microdroplets Drop-seq research 10K $$ 3' Macosko et al. 2015 microdroplets Indrop research 10K $$ 3' Klein et al. 2015 nanowells Seq-Well research 10K $ 3' Gierahn et al. 2017 nanowells Takara Wafergen commercial 1.8K $$$$ 3', full-length Goldstein et al. 2017 nanowells cytoseq research 100K $$ 3' Fodor et al. 2015 FACS smart-seq2 research 384 $ full-length Ramskold et al. 2012 FACS sci-RNA-seq research 100K $$ 3' Cao et al. 2017 scEpigenomics microdroplets 10X Chromium ATAC commercial 10K $$$$ tagmentation Satpathy et al. 2019 FACS ATAC tagmentation research 384 $ tagmentation Buenrostro et al. 2015 FACS dscATAC-seq research 100K $ tagmentation Cusavonich et al. 2015 micromanipulation scRBBS research 100 $$ RBBS Guo et al. 2013 scDNA-seq microdroplets 10X Chromium CNV commercial 10K $$$$ MDA Andor et al. 2018 microdroplets Mission Bio Tapestri commercial 10K $$$ amplicon PCR Lan et al. 2017 nanowells Wafergen Takara commercial/res 1.8K $$$$ tagmentation Laks et al. 2019 Microfluidics tagmentation research 200 $$ tagmentation Zahn et al. 2017 FACS sci-seq research 100K $$ tagmentation Adey et al. 2017 Open in a separate window Technologies for High-Throughput Single Cell Sequencing For epigenomic profiling, scATAC-seq has become the most widely used assay to measure chromatin accessibility of single cells.

Techniques: Micromanipulation, Amplification