https://cancerbiologyprogram.med.wayne.edu/pdfs/hallmarks_cancer_article.pdf

Hallmarks of cancer: the next generation.

Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2-5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

/pdf/fastp-an-ultra-fast-all-in-one-fastq-preprocessor-ehzxan9vkg.pdf

fastp: an ultra-fast all-in-one FASTQ preprocessor.

Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high level programming languages (e.g., Python and Java) and provide limited multithreading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results: We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multithreading support. Based on our evaluation, fastp is 2 to 5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and Implementation: The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp

https://www.biorxiv.org/content/biorxiv/early/2018/03/01/274100.full.pdf

fastp: an ultra-fast all-in-one FASTQ preprocessor

Evolution of Protein Molecules

AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore

PL02-05 

All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Patterns of Somatic Mutation in Human Cancer Genomes

Next-generation DNA sequencing promises to revolutionize clinical medicine and basic research. However, while this technology has the capacity to generate hundreds of billions of nucleotides of DNA sequence in a single experiment, the error rate of ∼1% results in hundreds of millions of sequencing mistakes. These scattered errors can be tolerated in some applications but become extremely problematic when “deep sequencing” genetically heterogeneous mixtures, such as tumors or mixed microbial populations. To overcome limitations in sequencing accuracy, we have developed a method termed Duplex Sequencing. This approach greatly reduces errors by independently tagging and sequencing each of the two strands of a DNA duplex. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors result in mutations in only one strand and can thus be discounted as technical error. We determine that Duplex Sequencing has a theoretical background error rate of less than one artifactual mutation per billion nucleotides sequenced. In addition, we establish that detection of mutations present in only one of the two strands of duplex DNA can be used to identify sites of DNA damage. We apply the method to directly assess the frequency and pattern of random mutations in mitochondrial DNA from human cells.

https://www.pnas.org/content/pnas/109/36/14508.full.pdf

Detection of ultra-rare mutations by next-generation sequencing

Duplex Sequencing (DS) is a next-generation sequencing methodology capable of detecting a single mutation among >1 × 10(7) wild-type nucleotides, thereby enabling the study of heterogeneous populations and very-low-frequency genetic alterations. DS can be applied to any double-stranded DNA sample, but it is ideal for small genomic regions of <1 Mb in size. The method relies on the ligation of sequencing adapters harboring random yet complementary double-stranded nucleotide sequences to the sample DNA of interest. Individually labeled strands are then PCR-amplified, creating sequence 'families' that share a common tag sequence derived from the two original complementary strands. Mutations are scored only if the variant is present in the PCR families arising from both of the two DNA strands. Here we provide a detailed protocol for efficient DS adapter synthesis, library preparation and target enrichment, as well as an overview of the data analysis workflow. The protocol typically takes 1-3 d.

Detecting ultralow-frequency mutations by Duplex Sequencing

Despite the remarkable throughput of next-generation sequencing technologies, standard techniques are limited by the difficulty in distinguishing sequencing errors from genuine low-frequency DNA variants within heterogeneous cellular or molecular populations. This Review discusses sequencing methodologies and bioinformatic strategies that have been devised for the reliable detection of rare mutations and describes various important applications in diverse fields including cancer, ageing and metagenomics. Mutations, the fuel of evolution, are first manifested as rare DNA changes within a population of cells. Although next-generation sequencing (NGS) technologies have revolutionized the study of genomic variation between species and individual organisms, most have limited ability to accurately detect and quantify rare variants among the different genome copies in heterogeneous mixtures of cells or molecules. We describe the technical challenges in characterizing subclonal variants using conventional NGS protocols and the recent development of error correction strategies, both computational and experimental, including consensus sequencing of single DNA molecules. We also highlight major applications for low-frequency mutation detection in science and medicine, describe emerging methodologies and provide our vision for the future of DNA sequencing.

Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations.

Mitochondrial DNA (mtDNA) is believed to be highly vulnerable to age-associated damage and mutagenesis by reactive oxygen species (ROS). However, somatic mtDNA mutations have historically been difficult to study because of technical limitations in accurately quantifying rare mtDNA mutations. We have applied the highly sensitive Duplex Sequencing methodology, which can detect a single mutation among >107 wild type molecules, to sequence mtDNA purified from human brain tissue from both young and old individuals with unprecedented accuracy. We find that the frequency of point mutations increases ∼5-fold over the course of 80 years of life. Overall, the mutation spectra of both groups are comprised predominantly of transition mutations, consistent with misincorporation by DNA polymerase γ or deamination of cytidine and adenosine as the primary mutagenic events in mtDNA. Surprisingly, G→T mutations, considered the hallmark of oxidative damage to DNA, do not significantly increase with age. We observe a non-uniform, age-independent distribution of mutations in mtDNA, with the D-loop exhibiting a significantly higher mutation frequency than the rest of the genome. The coding regions, but not the D-loop, exhibit a pronounced asymmetric accumulation of mutations between the two strands, with G→A and T→C mutations occurring more often on the light strand than the heavy strand. The patterns and biases we observe in our data closely mirror the mutational spectrum which has been reported in studies of human populations and closely related species. Overall our results argue against oxidative damage being a major driver of aging and suggest that replication errors by DNA polymerase γ and/or spontaneous base hydrolysis are responsible for the bulk of accumulating point mutations in mtDNA.

/pdf/ultra-sensitive-sequencing-reveals-an-age-related-increase-50794tpqm3.pdf

Ultra-Sensitive Sequencing Reveals an Age-Related Increase in Somatic Mitochondrial Mutations That Are Inconsistent with Oxidative Damage

Cancer recapitulates Darwinian evolution. Mutations acquired during life that provide cells with a growth or survival advantage will preferentially multiply to form a tumor. As a result of The Cancer Genome Atlas Project, we have gathered detailed information on the nucleotide sequence changes in a number of human cancers. The sources of mutations in cancer are diverse, and the complexity of those found to be clonally present in tumors has increasingly made it difficult to identify key rate-limiting genes for tumor growth that could serve as potential targets for directed therapies. The impact of DNA sequencing on future cancer research and personalized therapy is likely to be profound and merits critical evaluation.

https://depts.washington.edu/loeblabs/pdf/2009_Salk.pdf

Jesse J. Salk

Papers

Detection of ultra-rare mutations by next-generation sequencing

Detecting ultralow-frequency mutations by Duplex Sequencing

Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations.

Ultra-Sensitive Sequencing Reveals an Age-Related Increase in Somatic Mitochondrial Mutations That Are Inconsistent with Oxidative Damage

Mutational Heterogeneity in Human Cancers: Origin and Consequences