Novel Sequence Discovery by Subtractive Genomics

Summary

The purpose of this protocol is to use a combination of computational and bench research to find novel sequences that cannot be easily separated from a co-purifying sequence, which may be only partially known.

Abstract

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a larger genomic context. Subtractive genomics enables a researcher to isolate a target sequence of interest (T) by comprehensive sequencing and subtracting out known genetic elements (reference, R). The method can be used to identify novel sequences such as mitochondria, chloroplasts, viruses, or germline restricted chromosomes, and is particularly useful when T cannot be easily isolated from R. Beginning with the comprehensive genomic data (R + T), the method uses Basic Local Alignment Search Tool (BLAST) against a reference sequence, or sequences, to remove the matching known sequences (R), leaving behind the target (T). For subtraction to work best, R should be a relatively complete draft that is missing T. Since sequences remaining after subtraction are tested through quantitative Polymerase Chain Reaction (qPCR), R does not need to be complete for the method to work. Here we link computational steps with experimental steps into a cycle that can be iterated as needed, sequentially removing multiple reference sequences and refining the search for T. The advantage of subtractive genomics is that a completely novel target sequence can be identified even in cases in which physical purification is difficult, impossible, or expensive. A drawback of the method is finding a suitable reference for subtraction and obtaining T-positive and negative samples for qPCR testing. We describe our implementation of the method in the identification of the first gene from the germline-restricted chromosome of zebra finch. In that case computational filtering involved three references (R), sequentially removed over three cycles: an incomplete genomic assembly, raw genomic data, and transcriptomic data.

Introduction

The purpose of this method is to identify a novel target (T) genomic sequence, either DNA or RNA, from a genomic context, or reference (R) (Figure 1). The method is most useful if the target cannot be physically separated, or it would be expensive to do so. Only a few organisms have perfectly finished genomes for subtraction, so a key innovation of our method is the combination of computational and bench methods into a cycle enabling researchers to isolate target sequences when the reference is imperfect, or a draft genome from a non-model organism. At the end of a cycle, qPCR testing is used to determine whether more subtraction is neede....

Protocol

1. De novo Assemble Starting Sequence

NOTE: Any Next-Generation Sequence (NGS) data can be used, as long as an assembly can be produced from those data. Suitable input data includes Illumina, PacBio, or Oxford Nanopore reads assembled into a fasta file. For concreteness, this section describes an Illumina-based transcriptomic assembly specific to the zebra finch study we performed⁷; however be aware that the specifics will vary by project. For our example project, raw data were derived from a MiSeq and approximately 10 million paired reads were obtained from each sample.

1. Use Trimmomatic 0.32

Results

After running BLAST, the output file will have a list of sequences from the query that match the database. After Python subtraction, a number of nonmatching sequences will be obtained, and tested by qPCR. The results of this, and next steps, are discussed below.

Negative result. There are two possible negative results that can be seen after BLAST to the reference sequence. There may be no BLAST results, meaning .......

Discussion

While subtractive genomics is powerful, it is not a cookie-cutter approach, requiring customization at several key steps, and careful selection of reference sequences and test samples. If the query assembly is of poor quality, filtering steps might only isolate assembly artifacts. Therefore, it is important to thoroughly validate the de novo assembly using an appropriate validation protocol to the specific project. For RNA-seq, guidelines are provided on the Trinity website¹⁸ and for DNA,.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Accustart II Taq DNA Polymerase	Quanta Bio	95141
Blasic Local Alignment Search Tool (BLAST)			https://github.com/trinityrnaseq/trinityrnaseq/wiki/Transcriptome-Assembly-Quality-Assessment
Bowtie 2			https://www.python.org/download/releases/2.7/
BWA-MEM v. 0.7.12			https://github.com/BenLangmead/bowtie2
Geneious			https://blast.ncbi.nlm.nih.gov/Blast.cgi
PEAR v. 0.9.6			http://www.mybiosoftware.com/reptile-1-1-short-read-error-correction.html
Personal Computer	Biomatters		http://www.geneious.com/
PowerSYBR qPCR mix	ThermoFisher	4367659
Python v. 2.7			https://sco.h-its.org/exelixis/web/software/pear/
Reptile v.1.1			https://alurulab.cc.gatech.edu/reptile
Stratagene Mx3005P	Agilent Technologies	401456
TransDecoder v. 3.0.1			https://sourceforge.net/projects/bio-bwa/files/
Trinity v. 2.4.0			https://github.com/TransDecoder/TransDecoder/wiki