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High-throughput sequencing has the power to reveal the nature of adaptive immunity as represented by the full complexity of T-cell receptor (TCR) and antibody (IG) repertoires, but is at present severely compromised by the quantitative bias, bottlenecks, and accumulated errors that inevitably occur in the course of library preparation and sequencing. Here we report an optimized protocol for the unbiased preparation of TCR and IG cDNA libraries for high-throughput sequencing, starting from thousands or millions of live cells in an investigated sample. Critical points to control are revealed, along with tips that allow researchers to minimize quantitative bias, accumulated errors, and cross-sample contamination at each stage, and to enhance the subsequent bioinformatic analysis. The protocol is simple, reliable, and can be performed in 1-2 days.

Next generation sequencing (NGS) methods have allowed for unprecedented genomic characterization of acute myeloid leukemia (AML) over the last several years. Further advances in NGS-based methods including error correction using unique molecular identifiers (UMIs) have more recently enabled the use of NGS-based measurable residual disease (MRD) detection. This review focuses on the use of NGS-based MRD detection in AML, including basic methodologies and clinical applications.

Present status of P. oceanica genomics During last years, the research for coding sequences in P. oceanica has proceeded through the production of both differential and EST libraries for obtaining sequences to be used in analyses of environmental adaptation of Posidonia meadows, especially associated with different light and temperature regimes and other potential stress-responsive gene networks. Zostera marina sequencing initiative has started in 2010 under the guide of Dr. J. Olsen (University of Groningen) and it will greatly increase basic knowledge of seagrasses, allowing a deeper knowledge of seagrass biology and favoring studies in which analyses of genetic variability will be associated to the functional significance of differences. In model organisms as the mouse and Arabidopsis, genetic studies on development have shown that retroelements play a role in the epigenetic settings of the genome, both globally, regulating chromatin organization in the nucleus (Van-Driel et al.,2003), and locally, as control elements of the expression of genes (Song et al.,2004). Applications include precise quantification of RNA transcripts, measured through their sequence, without the probe hybridization employed in DNA chip techniques; identification and analysis of DNA regions that interact with regulatory proteins in functional regulation of gene expression; recovery and analysis of all components of a gene family.

This chapter contains sections titled: Introduction Next‐Generation Sequencing Technology Applications of Next‐generation Sequencing in Rainbow Trout Conclusion and Future Perspectives References

By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.

Background As a result of its simplicity and high efficiency, the CRISPR-Cas system has been widely used as a genome editing tool. Recently, CRISPR base editors, which consist of deactivated Cas9 (dCas9) or Cas9 nickase (nCas9) linked with a cytidine or a guanine deaminase, have been developed. Base editing tools will be very useful for gene correction because they can produce highly specific DNA substitutions without the introduction of any donor DNA, but dedicated web-based tools to facilitate the use of such tools have not yet been developed. Results We present two web tools for base editors, named BE-Designer and BE-Analyzer. BE-Designer provides all possible base editor target sequences in a given input DNA sequence with useful information including potential off-target sites. BE-Analyzer, a tool for assessing base editing outcomes from next generation sequencing (NGS) data, provides information about mutations in a table and interactive graphs. Furthermore, because the tool runs client-side, large amounts of targeted deep sequencing data (< 1 GB) do not need to be uploaded to a server, substantially reducing running time and increasing data security. BE-Designer and BE-Analyzer can be freely accessed at http://www.rgenome.net/be-designer/ and http://www.rgenome.net/be-analyzer /, respectively. Conclusion We develop two useful web tools to design target sequence (BE-Designer) and to analyze NGS data from experimental results (BE-Analyzer) for CRISPR base editors.

Background Some applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling. Results For each single or pair of FastQ files, AfterQC filters out bad reads, detects and eliminates sequencer’s bubble effects, trims reads at front and tail, detects the sequencing errors and corrects part of them, and finally outputs clean data and generates HTML reports with interactive figures. AfterQC can run in batch mode with multiprocess support, it can run with a single FastQ file, a single pair of FastQ files (for pair-end sequencing), or a folder for all included FastQ files to be processed automatically. Based on overlapping analysis, AfterQC can estimate the sequencing error rate and profile the error transform distribution. The results of our error profiling tests show that the error distribution is highly platform dependent. Conclusion Much more than just another new quality control (QC) tool, AfterQC is able to perform quality control, data filtering, error profiling and base correction automatically. Experimental results show that AfterQC can help to eliminate the sequencing errors for pair-end sequencing data to provide much cleaner outputs, and consequently help to reduce the false-positive variants, especially for the low-frequency somatic mutations. While providing rich configurable options, AfterQC can detect and set all the options automatically and require no argument in most cases.

Background The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs. Results Existing tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored. Conclusions Je is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je . Je can also be easily installed in Galaxy through the Galaxy toolshed.

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.