scholarly journals SMARTcleaner: identify and clean off-target signals in SMART ChIP-seq analysis

2018 ◽  
Author(s):  
Dejian Zhao ◽  
Deyou Zheng

AbstractBackgroundNoises and artifacts may arise in several steps of the next-generation sequencing (NGS) process. Recently, a NGS library preparation method called SMART, orSwitchingMechanismAt the 5’ end of theRNATranscript, is introduced to prepare ChIP-seq (chromatin immunoprecipitation and deep sequencing) libraries from small amount of DNA material. The protocol adds Ts to the 3’ end of DNA templates, which is subsequently recognized and used by SMART poly(dA) primers for reverse transcription and then addition of PCR primers and sequencing adapters. The poly(dA) primers, however, can anneal to poly(T) sequences in a genome and amplify DNA fragments that are not enriched in the immunoprecipitated DNA templates. This off-target amplification results in false signals in the ChIP-seq data.ResultsHere, we show that the off-target ChIP-seq reads derived from false amplification of poly(T/A) genomic sequences have unique and strand-specific features. Accordingly, we develop a tool (called “SMARTcleaner”) that can exploit the features to remove SMART ChIP-seq artifacts. Application of SMARTcleaner to several SMART ChIP-seq datasets demonstrates that it can remove reads from off-target amplification effectively, leading to improved ChIP-seq peaks and results.ConclusionsSMARTcleaner could identify and clean the false signals in SMART-based ChIP-seq libraries, leading to improvement in peak calling, and downstream data analysis and interpretation.

2019 ◽  
Author(s):  
Kate D. Meyer

Abstract m6A is the most abundant internal mRNA modification and plays diverse roles in gene expression regulation. Much of our current knowledge about m6A has been driven by recent advances in the ability to detect this mark transcriptome-wide. Antibody-based approaches have been the method of choice for global m6A mapping studies. These methods rely on m6A antibodies to immunoprecipitate methylated RNAs, followed by next-generation sequencing to identify m6A-containing transcripts1,2. While these methods enabled the first identification of m6A sites transcriptome-wide and have dramatically improved our ability to study m6A, they suffer from several limitations. These include requirements for high amounts of input RNA, costly and time-consuming library preparation, high variability across studies, and m6A antibody cross-reactivity with other modifications. Here, we describe DART-Seq (deamination adjacent to RNA modification targets), an antibody-free method for global m6A detection. In DART-Seq, the C to U deaminating enzyme, APOBEC1, is fused to the m6A-binding YTH domain. This fusion protein is then introduced to cellular RNA either through overexpression in cells or with in vitro assays, and subsequent deamination of m6A-adjacent cytidines is then detected by RNA sequencing to identify m6A sites. DART-Seq can successfully map m6A sites throughout the transcriptome using as little as 10 nanograms of total cellular RNA, and it is compatible with any standard RNA-seq library preparation method.


2016 ◽  
Vol 45 (6) ◽  
pp. e36-e36 ◽  
Author(s):  
Amanda Raine ◽  
Erika Manlig ◽  
Per Wahlberg ◽  
Ann-Christine Syvänen ◽  
Jessica Nordlund

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Emily A Saunderson ◽  
Ann-Marie Baker ◽  
Marc Williams ◽  
Kit Curtius ◽  
J Louise Jones ◽  
...  

Abstract The desire to analyse limited amounts of biological material, historic samples and rare cell populations has collectively driven the need for efficient methods for whole genome sequencing (WGS) of limited amounts of poor quality DNA. Most protocols are designed to recover double-stranded DNA (dsDNA) by ligating sequencing adaptors to dsDNA with or without subsequent polymerase chain reaction amplification of the library. While this is sufficient for many applications, limited DNA requires a method that can recover both single-stranded DNA (ssDNA) and dsDNA. Here, we present a WGS library preparation method, called ‘degraded DNA adaptor tagging’ (DDAT), adapted from a protocol designed for whole genome bisulfite sequencing. This method uses two rounds of random primer extension to recover both ssDNA and dsDNA. We show that by using DDAT we can generate WGS data from formalin-fixed paraffin-embedded (FFPE) samples using as little as 2 ng of highly degraded DNA input. Furthermore, DDAT WGS data quality was higher for all FFPE samples tested compared to data produced using a standard WGS library preparation method. Therefore, the DDAT method has potential to unlock WGS data from DNA previously considered impossible to sequence, broadening opportunities to understand the role of genetics in health and disease.


2020 ◽  
Vol 6 (3) ◽  
pp. 32 ◽  
Author(s):  
Anna R. Dahlgren ◽  
Erica Y. Scott ◽  
Tamer Mansour ◽  
Erin N. Hales ◽  
Pablo J. Ross ◽  
...  

Long non-coding RNAs (lncRNAs) are untranslated regulatory transcripts longer than 200 nucleotides that can play a role in transcriptional, post-translational, and epigenetic regulation. Traditionally, RNA-sequencing (RNA-seq) libraries have been created by isolating transcriptomic RNA via poly-A+ selection. In the past 10 years, methods to perform ribosomal RNA (rRNA) depletion of total RNA have been developed as an alternative, aiming for better coverage of whole transcriptomic RNA, both polyadenylated and non-polyadenylated transcripts. The purpose of this study was to determine which library preparation method is optimal for lncRNA investigations in the horse. Using liver and cerebral parietal lobe tissues from two healthy Thoroughbred mares, RNA-seq libraries were prepared using standard poly-A+ selection and rRNA-depletion methods. Averaging the two biologic replicates, poly-A+ selection yielded 327 and 773 more unique lncRNA transcripts for liver and parietal lobe, respectively. More lncRNA were found to be unique to poly-A+ selected libraries, and rRNA-depletion identified small nucleolar RNA (snoRNA) to have a higher relative expression than in the poly-A+ selected libraries. Overall, poly-A+ selection provides a more thorough identification of total lncRNA in equine tissues while rRNA-depletion may allow for easier detection of snoRNAs.


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