It has been another productive week in the world of science. To keep up with all the advancements and announcements, we’ve put together our list of the top 5 things you should read about this week. If there’s anything we’ve missed, log in and tell us in the comments below!
1. Archival single-cell genomics reveals persistent subclones during DCIS progression
Our first recommendation is a paper from the well-known single-cell lab of Nicholas Navin.
Summary: This study introduces Arc-well, a high-throughput single-cell DNA sequencing method specifically designed to be compatible with archival formalin-fixed paraffin-embedded (FFPE) materials, which are challenging to work with. The researcher team successfully used Arc-well to profile 40,330 single cells from a diverse set of samples, including cell lines, frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors that have been stored for durations ranging from 3 to 31 years. In the study, an analysis of 10 patients with matched ductal carcinoma in situ (DCIS, a common precursor of invasive breast cancer) and subsequent recurrent cancers (2-16 years later) demonstrated that many of the primary DCIS cases had already experienced whole-genome doubling and clonal diversification. Moreover, the researchers found persistent subclones in the recurrent cancers that shared genomic lineages with the initial DCIS cases. Evolutionary analysis suggests that the majority of the DCIS cases in the studied cohort underwent an evolutionary bottleneck, and specific chromosomal aberrations in the persistent subclones were identified as being associated with cancer recurrence.
Read the original study here.
2. Genome assembly in the telomere-to-telomere era
Number two is a review from the prolific bioinformaticians, Heng Li and Richard Durbin.
Summary: The field of genome assembly has undergone a significant transformation with the advent of long-read sequencing technologies. Traditionally, genome assembly has been a complex and fragmented process, but recent advances have brought it closer to complete, chromosome-level assembly, also known as telomere-to-telomere (T2T) assembly. The availability of PacBio HiFi and ONT ultra-long reads has been instrumental in this shift, allowing researchers to assemble large genomes of diploid mammals and other organisms with unprecedented quality. Despite these improvements, full T2T assembly of all chromosomes using current data remains a challenge, as existing assembly algorithms may not fully resolve complex genomic regions or deal efficiently with polyploid genomes, including cancer genomes and metagenomic samples. Furthermore, while assembly quality has dramatically improved, tools for genome alignment and annotation have not kept pace, highlighting the need for continued development in these areas to fully capitalize on the advances in genome assembly.
Read the original pre-print here.
3. CasPEDIA - Cas Protein Effector Database of Information and Assessment
Nobel Laureate Jennifer Doudna, and her lab at UC Berkeley released a new database.
Summary: CasPEDIA is an encyclopedia of Class 2 CRISPR systems, presented in a wiki format that offers comprehensive descriptions of enzyme activities, structures, and sequences, supported by an extensive literature review for each nuclease. The database introduces a unique identifier system, known as CasIDs, to organize Cas enzymes based on their functional properties. Inspired by the ENZYME Classification (E.C.) system, CasID nomenclature uses a three-digit format to concisely describe effector properties, thereby enabling researchers to rapidly identify appropriate enzymes for their specific use cases. These CasIDs can be used to navigate the CasPEDIA website through direct searches in the search bar and by using the Tool Finder feature. The website also includes a FAQ section to assist users in navigating CasPEDIA, and information for those interested in becoming a curator. For individuals seeking foundational knowledge on CRISPR, the site recommends CRISPRpedia, created by The Innovative Genomics Institute.
Discover this new resource here.
4. Single-nucleotide variant calling in single-cell sequencing data with Monopogen
The Ken Chen Lab has created another notable computational tool.
Summary: Monopogen is a computational tool designed to detect single-nucleotide variants (SNVs) from single-cell sequencing data, addressing the gap in understanding how a cell's genetic background influences its transcriptional and epigenetic profiles. Utilizing external reference panels, Monopogen leverages linkage disequilibrium data to identify germline SNVs with a genotyping accuracy of 95%. Additionally, it detects putative somatic SNVs by examining allele cosegregating patterns at the cell population level. This tool is designed to work with various single-cell omics data types, including transcriptomic and epigenomic sequencing data. It enables global and local ancestry inference, allowing for the identification of admixed samples and the mapping of genetic ancestry, which is essential for avoiding biases in human genetic studies. Monopogen’s application extends to identifying variants associated with specific cellular traits, such as cardiomyocyte metabolism levels, and its capabilities support the advancement of clonal lineage tracing in diseases like cancer. Thus, Monopogen integrates population genomics with single-cell analysis to help reveal genetic determinants of cellular processes and holds significant potential in understanding complex traits in human populations and diseases.
Learn more about Monopogen in the original publication here.
5. UniAligner: a parameter-free framework for fast sequence alignment
Formerly, TandemAligner, Andrey Bzikadze and Pavel Pevzner created their parameter-free sequence alignment algorithm.
Summary: UniAligner is a parameter-free sequence alignment algorithm designed to address the challenges associated with analyzing variations in extra-long tandem repeats (ETRs), including centromeres, in genomic sequences. Traditional alignment approaches, such as the Smith-Waterman algorithm, struggle to construct biologically meaningful alignments of ETRs. UniAligner introduces a sequence-dependent alignment scoring system that automatically adjusts based on the pair of sequences being compared. It prioritizes matches of rare substrings, which are more likely to be indicative of the evolutionary relationship between two sequences. When applied to human centromeres, UniAligner revealed an extremely high rate of large duplications and deletions, suggesting that centromeres may be among the most rapidly evolving regions of the human genome in terms of structural organization.
Read about UniAligner in the original journal here.
Bonus article: The specious art of single-cell genomics
Tara Chari and Lior Pachter cover important problems with single-cell sequencing analysis.
Read their full discussion here.
1. Archival single-cell genomics reveals persistent subclones during DCIS progression
Our first recommendation is a paper from the well-known single-cell lab of Nicholas Navin.
Summary: This study introduces Arc-well, a high-throughput single-cell DNA sequencing method specifically designed to be compatible with archival formalin-fixed paraffin-embedded (FFPE) materials, which are challenging to work with. The researcher team successfully used Arc-well to profile 40,330 single cells from a diverse set of samples, including cell lines, frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors that have been stored for durations ranging from 3 to 31 years. In the study, an analysis of 10 patients with matched ductal carcinoma in situ (DCIS, a common precursor of invasive breast cancer) and subsequent recurrent cancers (2-16 years later) demonstrated that many of the primary DCIS cases had already experienced whole-genome doubling and clonal diversification. Moreover, the researchers found persistent subclones in the recurrent cancers that shared genomic lineages with the initial DCIS cases. Evolutionary analysis suggests that the majority of the DCIS cases in the studied cohort underwent an evolutionary bottleneck, and specific chromosomal aberrations in the persistent subclones were identified as being associated with cancer recurrence.
Read the original study here.
2. Genome assembly in the telomere-to-telomere era
Number two is a review from the prolific bioinformaticians, Heng Li and Richard Durbin.
Summary: The field of genome assembly has undergone a significant transformation with the advent of long-read sequencing technologies. Traditionally, genome assembly has been a complex and fragmented process, but recent advances have brought it closer to complete, chromosome-level assembly, also known as telomere-to-telomere (T2T) assembly. The availability of PacBio HiFi and ONT ultra-long reads has been instrumental in this shift, allowing researchers to assemble large genomes of diploid mammals and other organisms with unprecedented quality. Despite these improvements, full T2T assembly of all chromosomes using current data remains a challenge, as existing assembly algorithms may not fully resolve complex genomic regions or deal efficiently with polyploid genomes, including cancer genomes and metagenomic samples. Furthermore, while assembly quality has dramatically improved, tools for genome alignment and annotation have not kept pace, highlighting the need for continued development in these areas to fully capitalize on the advances in genome assembly.
Read the original pre-print here.
3. CasPEDIA - Cas Protein Effector Database of Information and Assessment
Nobel Laureate Jennifer Doudna, and her lab at UC Berkeley released a new database.
Summary: CasPEDIA is an encyclopedia of Class 2 CRISPR systems, presented in a wiki format that offers comprehensive descriptions of enzyme activities, structures, and sequences, supported by an extensive literature review for each nuclease. The database introduces a unique identifier system, known as CasIDs, to organize Cas enzymes based on their functional properties. Inspired by the ENZYME Classification (E.C.) system, CasID nomenclature uses a three-digit format to concisely describe effector properties, thereby enabling researchers to rapidly identify appropriate enzymes for their specific use cases. These CasIDs can be used to navigate the CasPEDIA website through direct searches in the search bar and by using the Tool Finder feature. The website also includes a FAQ section to assist users in navigating CasPEDIA, and information for those interested in becoming a curator. For individuals seeking foundational knowledge on CRISPR, the site recommends CRISPRpedia, created by The Innovative Genomics Institute.
Discover this new resource here.
4. Single-nucleotide variant calling in single-cell sequencing data with Monopogen
The Ken Chen Lab has created another notable computational tool.
Summary: Monopogen is a computational tool designed to detect single-nucleotide variants (SNVs) from single-cell sequencing data, addressing the gap in understanding how a cell's genetic background influences its transcriptional and epigenetic profiles. Utilizing external reference panels, Monopogen leverages linkage disequilibrium data to identify germline SNVs with a genotyping accuracy of 95%. Additionally, it detects putative somatic SNVs by examining allele cosegregating patterns at the cell population level. This tool is designed to work with various single-cell omics data types, including transcriptomic and epigenomic sequencing data. It enables global and local ancestry inference, allowing for the identification of admixed samples and the mapping of genetic ancestry, which is essential for avoiding biases in human genetic studies. Monopogen’s application extends to identifying variants associated with specific cellular traits, such as cardiomyocyte metabolism levels, and its capabilities support the advancement of clonal lineage tracing in diseases like cancer. Thus, Monopogen integrates population genomics with single-cell analysis to help reveal genetic determinants of cellular processes and holds significant potential in understanding complex traits in human populations and diseases.
Learn more about Monopogen in the original publication here.
5. UniAligner: a parameter-free framework for fast sequence alignment
Formerly, TandemAligner, Andrey Bzikadze and Pavel Pevzner created their parameter-free sequence alignment algorithm.
Summary: UniAligner is a parameter-free sequence alignment algorithm designed to address the challenges associated with analyzing variations in extra-long tandem repeats (ETRs), including centromeres, in genomic sequences. Traditional alignment approaches, such as the Smith-Waterman algorithm, struggle to construct biologically meaningful alignments of ETRs. UniAligner introduces a sequence-dependent alignment scoring system that automatically adjusts based on the pair of sequences being compared. It prioritizes matches of rare substrings, which are more likely to be indicative of the evolutionary relationship between two sequences. When applied to human centromeres, UniAligner revealed an extremely high rate of large duplications and deletions, suggesting that centromeres may be among the most rapidly evolving regions of the human genome in terms of structural organization.
Read about UniAligner in the original journal here.
Bonus article: The specious art of single-cell genomics
Tara Chari and Lior Pachter cover important problems with single-cell sequencing analysis.
Read their full discussion here.