Sequence analysis is the process of analyzing DNA, RNA or peptide sequences using a wide range of methodologies in order to understand their functions, structures or evolution history. Next generation sequencing (NGS) technologies generate large-scale sequence data of high coverage and nucleotide level resolution at low costs, benefiting a variety of research areas such as gene expression profiling, metagenomic annotation, ncRNA identification, etc. Therefore, functional analysis of NGS sequences becomes increasingly important because it provides insightful information, such as gene expression, protein composition, and phylogenetic complexity, of the species from which the sequences are generated. One basic step during the functional analysis is to classify genomic sequences into different functional categories, such as protein families or protein domains (or domains for short), which are independent functional units in a majority of annotated protein sequences. The state-of-the-art method for protein domain analysis is based on comparative sequence analysis, which classifies query sequences into annotated protein or domain databases. There are two types of domain analysis methods, pairwise alignment and profile-based similarity search. The first one uses pairwise alignment tools such as BLAST to search query genomic sequences against reference protein sequences in databases such as NCBI-nr. The second one uses profile HMM-based tools such as HMMER to classify query sequences into annotated domain families such as Pfam. Compared to the first method, the profile HMM-based method has smaller search space and higher sensitivity with remote homolog detection. Therefore, I focus on profile HMM-based protein domain analysis.There are several challenges with protein domain analysis of NGS sequences. First, sequences generated by some NGS platforms such as pyrosequencing have relatively high error rates, making it difficult to classify the sequences into their native domain families. Second, existing protein domain analysis tools have low sensitivity with short query sequences and poorly conserved domain families. Third, the volume of NGS data is usually very large, making it difficult to assemble short reads into longer contigs. In this work, I focus on addressing these three challenges using different methods. To be specific, we have proposed four tools, HMM-FRAME, MetaDomain, SALT, and SAT-Assembler. HMM-FRAME focuses on detecting and correcting frameshift errors in sequences generated by pyrosequencing technology, thus accurately classifying metagenomic sequences containing frameshift errors into their native protein domain families. MetaDomain and SALT are both designed for short reads generated by NGS technologies. MetaDomain uses relaxed position-specific score thresholds and alignment positions to increase the sensitivity while keeping the false positive rate at a low level. SALT combines both position-specific score thresholds and graph algorithms and achieves higher accuracy than MetaDomain. SAT-Assembler conducts targeted gene assembly from large-scale NGS data. It has smaller memory usage, higher gene coverage, and lower chimera rate compared with existing tools. Finally, I will make a conclusion on my work and briefly talk about some future work
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