Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

The last twenty years have seen a high intervention of precision genomics that has led to the emergence of personalized genomics that aims for genetic feature-tailored health care. New investigative methodologies such as genome-wide association studies (GWAS) have led to a large-scale contribution to population genomic data of complex diseases. Similarly, in the predictive bioinformatics arena, every day new technologies and algorithms are being developed to make better and more accurate prediction models. Machine learning and pattern discovery algorithms applied to genetic data along with polygenic risk scoring have become methods of choice to design and code novel software for the detection of SNPs. Such algorithms have become the next big thing in predictive multidimensional data analysis in genomics. This chapter aims to review the most common predictors and web resources that can be used for genetic data-based SNP prediction, especially related to humans and diseases. We sum up and provide links for a range of web servers, predictors and applications in SNP prediction. The tools provided herein would act as a beneficial asset for the researchers targeting the domain of SNP biology and predictive exploratory genomic data analysis of nucleotide variations.

Single nucleotide polymorphisms (SNPs) are the predominant variations found in human genomes, and till now, more than a hundred million have been reported. These variations are found to be associated with various phenotypes, drug responses, gene expression, and disease biology, including cancers. Recently, there has been an exponential growth in the genomic field, mainly due to the progress in next-generation sequencing (NGS) technologies and enhancements in the speed and power of computers leading to the generation of enormous data, including these genomic variations. This has led to corresponding advances in the detection of such nucleotide variations via genome-wide association studies (GWAS) and their applications in disease treatment or as biomarkers. It has become necessary to store, process, retrieve, and link these data so that they can be optimally used by all researchers universally. This led to the development of specialized bioinformatics databases and algorithms via collaborations and projects worldwide, with the National Institutes of Health (the USA) being the frontrunner. Some projects that were milestones in the SNP-related research include Human Genome Project, The Cancer Genome Atlas Program, and the 1000 Genomes Project. In this chapter, we will discuss predominately the available SNP-related resources, along with many supplementary links redirecting to corresponding web servers and their biological relevance or functional aspects of diseases, especially cancer.