scholarly journals Single Cell DNA Sequencing – from Analog to Digital

2017 ◽  
Vol 3 (1) ◽  
pp. 161-169 ◽  
Author(s):  
Larry Xi ◽  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Analog To Digital

scholarly journals Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leah L. Weber ◽  
Mohammed El-Kebir
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Evolutionary Process ◽  
Treatment Decision ◽  
Real Data ◽  
Current Data ◽  
Fast Method ◽  
Sequencing Data ◽  
Evolutionary Trajectory ◽  
Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

scholarly journals Modular barcode beads for microfluidic single cell genomics

2020 ◽  
Author(s):  
Cyrille L. Delley ◽  
Adam R. Abate
Keyword(s):  
Nucleic Acid ◽  
Dna Sequencing ◽  
Single Cell ◽  
Optimized Design ◽  
Single Cell Genomics ◽  
New Targets ◽  
Novel Targets ◽  
Rna And Dna ◽  
Modular Framework

AbstractBarcode beads allow efficient nucleic acid tagging in single cell genomics. Current barcode designs, however, are fabricated with a particular application in mind. Repurposing to novel targets, or altering to add additional targets as information is obtained is possible but the result is suboptimal. Here, we describe a modular framework that simplifies generation of multifunctional beads and allows their easy extension to new targets.One Sentence SummaryWe describe an optimized design for barcoding beads which are useful for single cell RNA and DNA sequencing.

scholarly journals Genomic Heterogeneity and Clonal Evolution in Gastroesophageal Junction Cancer Revealed by Single Cell DNA Sequencing

2021 ◽  
Vol 11 ◽  
Author(s):  
Qingke Duan ◽  
Chao Tang ◽  
Zhao Ma ◽  
Chuangui Chen ◽  
Xiaobin Shang ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Dna Sequencing ◽  
Single Cell ◽  
Single Cells ◽  
Clonal Evolution ◽  
Gastroesophageal Junction ◽  
Common Mutation ◽  
Node Metastasis

Gastroesophageal junction (GEJ) cancer is a tumor that occurs at the junction of stomach and esophagus anatomically. GEJ cancer frequently metastasizes to lymph nodes, however the heterogeneity and clonal evolution process are unclear. This study is the first of this kind to use single cell DNA sequencing to determine genomic variations and clonal evolution related to lymph node metastasis. Multiple Annealing and Looping Based Amplification Cycles (MALBAC) and bulk exome sequencing were performed to detect single cell copy number variations (CNVs) and single nucleotide variations (SNVs) respectively. Four GEJ cancer patients were enrolled with two (Pt.3, Pt.4) having metastatic lymph nodes. The most common mutation we found happened in the TTN gene, which was reported to be related with the tumor mutation burden in cancers. Significant intra-patient heterogeneity in SNVs and CNVs were found. We identified the SNV subclonal architecture in each tumor. To study the heterogeneity of CNVs, the single cells were sequenced. The number of subclones in the primary tumor was larger than that in lymph nodes, indicating the heterogeneity of primary site was higher. We observed two patterns of multi-station lymph node metastasis: one was skip metastasis and the other was to follow the lymphatic drainage. Taken together, our single cell genomic analysis has revealed the heterogeneity and clonal evolution in GEJ cancer.

P-061: Single-cell whole-exome DNA sequencing traces clonal trajectory in paired evolution of MGUS to multiple myeloma

2021 ◽  
Vol 21 ◽  
pp. S72
Author(s):  
Naser Ansari-Pour ◽  
Silvia Salatino ◽  
Nicola Weston-Bell ◽  
Dean Bryant ◽  
Luz Yurany Moreno ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Dna Sequencing ◽  
Single Cell ◽  
Whole Exome

scholarly journals Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data

2020 ◽  
Vol 16 (7) ◽  
pp. e1008012 ◽  
Author(s):  
Xian F. Mallory ◽  
Mohammadamin Edrisi ◽  
Nicholas Navin ◽  
Luay Nakhleh
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Copy Number Aberration ◽  
Sequencing Data ◽  
Aberration Detection

Deconvolution of Clonal Heterogeneity in Acute Myeloid Leukemia using Single-Cell DNA Sequencing

2019 ◽  
Vol 19 ◽  
pp. S234
Author(s):  
Kiyomi Morita ◽  
Feng Wang ◽  
Katharina Jahn ◽  
Jack Kuipers ◽  
Yuanqing Yan ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Dna Sequencing ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Clonal Heterogeneity ◽  
Acute Myeloid

scholarly journals scHaplotyper: haplotype construction and visualization for genetic diagnosis using single cell DNA sequencing data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiqiang Yan ◽  
Xiaohui Zhu ◽  
Yuqian Wang ◽  
Yanli Nie ◽  
Shuo Guan ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Genetic Diagnosis ◽  
Sequencing Data
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