The expanding scope of DNA sequencing

Excitotoxicity, Oxytosis/Ferroptosis, and Neurodegeneration: Emerging Insights into Mitochondrial Mechanisms

Mitochondrial dysfunction plays a pivotal role in the development of age-related diseases, particularly neurodegenerative disorders. The etiology of mitochondrial dysfunction involves a multitude of factors that remain elusive. This review centers on elucidating the role(s) of excitotoxicity, oxytosis/ferroptosis and neurodegeneration within the context of mitochondrial bioenergetics, biogenesis, mitophagy and oxidative stress and explores their intricate interplay in the pathogenesis of neurodegenerative diseases. The effective coordination of mitochondrial turnover processes, notably mitophagy and biogenesis, is assumed to be critically important for cellular resilience and longevity. However, the age-associated decrease in mitophagy impedes the elimination of dysfunctional mitochondria, consequently impairing mitochondrial biogenesis. This deleterious cascade results in the accumulation of damaged mitochondria and deterioration of cellular functions. Both excitotoxicity and oxytosis/ferroptosis have been demonstrated to contribute significantly to the pathophysiology of neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's Disease (HD), Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). Excitotoxicity, characterized by excessive glutamate signaling, initiates a cascade of events involving calcium dysregulation, energy depletion, and oxidative stress and is intricately linked to mitochondrial dysfunction. Furthermore, emerging concepts surrounding oxytosis/ferroptosis underscore the importance of iron-dependent lipid peroxidation and mitochondrial engagement in the pathogenesis of neurodegeneration. This review not only discusses the individual contributions of excitotoxicity and ferroptosis but also emphasizes their convergence with mitochondrial dysfunction, a key driver of neurodegenerative diseases. Understanding the intricate crosstalk between excitotoxicity, oxytosis/ferroptosis, and mitochondrial dysfunction holds potential to pave the way for mitochondrion-targeted therapeutic strategies. Such strategies, with a focus on bioenergetics, biogenesis, mitophagy, and oxidative stress, emerge as promising avenues for therapeutic intervention.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Selective Sensing of DNA Nucleobases with Angular Discrimination

The fast and precise selective sensing of DNA nucleobases is a long-pursued method that can lead to huge advances in the field of genomics and have an impact on aspects such as the prevention of diseases, health enhancement, and, in general, all types of medical treatments. We present here a new type of nanoscale sensor based on carbon nanotubes with a specific geometry that can discriminate the type of nucleobase and also its angle of orientation. The proper differentiation of nucleobases is essential to clearly sequence DNA chains, while angular discrimination is key to improving the sensing selectivity. We perform first-principle and quantum transport simulations to calculate the transmission, conductance, and current of the nanotube-based nanoscale sensor in the presence of the four nucleotides (A, C, G, and T), each of them rotated 0, 90, 180, or 270°. Our results show that this system is able to effectively discriminate between the four nucleotides and their angle of orientation. We explain these findings in terms of the interaction between the phosphate group of the nucleotide and the nanotube wall. The phosphate specifically distorts the electronic structure of the nanotube depending on the distance and the orientation and leads to nontrivial changes in the transmission. This work provides a method for finer and more precise sequential DNA chains.

Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies

Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.

Towards Chinese text and DNA shift encoding scheme based on biomass plasmid storage

DNA, as the storage medium in organisms, can address the shortcomings of existing electromagnetic storage media, such as low information density, high maintenance power consumption, and short storage time. Current research on DNA storage mainly focuses on designing corresponding encoders to convert binary data into DNA base data that meets biological constraints. We have created a new Chinese character code table that enables exceptionally high information storage density for storing Chinese characters (compared to traditional UTF-8 encoding). To meet biological constraints, we have devised a DNA shift coding scheme with low algorithmic complexity, which can encode any strand of DNA even has excessively long homopolymer. The designed DNA sequence will be stored in a double-stranded plasmid of 744bp, ensuring high reliability during storage. Additionally, the plasmid's resistance to environmental interference ensuring long-term stable information storage. Moreover, it can be replicated at a lower cost.

Highly Sensitive and Selective DNA Sequencing Device Using Metal Adatom Adsorption on 2D Phosphorene

Two-dimensional (2D) material revolutionarily extends the technique capability of traditional nanopore/nanogap-based DNA sequencing devices. However, challenges associated with DNA sequencing on nanopores still remained in improving the sensitivity and specificity. Herein, by first-principles calculation, we theoretically studied the potential of transition-metal elements (Cr, Fe, Co, Ni, and Au) anchored on monolayer black phosphorene (BP) to act as all-electronic DNA sequencing devices. The spin-polarized band structures appeared in Cr-, Fe-, Co-, and Au-doped BP. Remarkably, the adsorption energy of nucleobases can be significantly enhanced on BP with Co, Fe, and Cr doping, which contribute to the enlarged current signal and lower noise levels. Furthermore, the order of nucleobases in terms of their adsorption energies onto the Cr@BP is C > A > G > T, which exhibits more distinct adsorption energies than Fe@BP or Co@BP. Therefore, Cr-doped BP is more effective to avoid ambiguity in recognizing various bases. We thus envisaged a possibility of a highly sensitive and selective DNA sequencing device based on phosphorene.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Towards a graphene semi/hybrid-nanogap: a new architecture for ultrafast DNA sequencing

The tremendous upsurge in the research of next-generation sequencing (NGS) methods has broadly been driven by the rise of the wonder material graphene and continues to dominate the futuristic approaches for fast and accurate DNA sequencing. The success of graphene has also triggered the search for many new potential NGS methods capable of ultrafast, reliable, and controlled DNA sequencing. The present study delves into the potential of a new NGS architecture utilizing graphene, namely, a 'semi/hybrid-nanogap' for the identification of DNA nucleobases with single-base resolution. In the framework of first-principles density functional theory methods, we have calculated the transmission function and current-voltage (I-V) characteristics which are of particular significance for DNA sequencing applications. It is noted that the interaction energy values are significantly reduced compared to the previously reported graphene nanodevices, which can lead to a controlled translocation during experimental measurements. Based on the transmission function, each nucleobase can be identified with pertinent sensitivity. It is noticed that the use of highly conductive nucleobase analogs can facilitate improved single nucleobase sensing by increasing the transmission sensitivity. Therefore, we believe that the present study opens up promising frontiers for sequencing applications.

1Progress, applications, challenges and prospects of protein purification technology

Protein is one of the most important biological macromolecules in life, which plays a vital role in cell growth, development, movement, heredity, reproduction and other life activities. High quality isolation and purification is an essential step in the study of the structure and function of target proteins. Therefore, the development of protein purification technologies has great theoretical and practical significance in exploring the laws of life activities and guiding production practice. Up to now, there is no forthcoming method to extract any proteins from a complex system, and the field of protein purification still faces significant opportunities and challenges. Conventional protein purification generally includes three steps: pretreatment, rough fractionation, and fine fractionation. Each of the steps will significantly affect the purity, yield and the activity of target proteins. The present review focuses on the principle and process of protein purification, recent advances, and the applications of these technologies in the life and health industry as well as their far-reaching impact, so as to promote the research of protein structure and function, drug development and precision medicine, and bring new insights to researchers in related fields.

RNA m6A reader IGF2BP3 promotes metastasis of triple-negative breast cancer via SLIT2 repression

Triple-negative breast cancer (TNBC) is a group of fatal malignancies characterized by high metastatic capacity, the underlying mechanisms of which remain largely elusive. We have found here that insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) is highly expressed in TNBC and correlates clinically with distant metastasis-free survival of TNBC patients. IGF2BP3 promotes the migration and invasion capabilities of TNBC cells dependent upon cellular RNA N6-methyladenosine (m6A) modification. Mechanistically, IGF2BP3 binds to and destabilizes m6A-methylated mRNA of the extracellular matrix glycoprotein, SLIT2, impairs its downstream signaling via the cognate receptor ROBO1, and consequently triggers the activation of canonical PI3K/AKT and MEK/ERK pathways. The IGF2BP3/SLIT2 axis is critically involved in the regulation of TNBC metastasis in vivo. These findings shed light into the regulatory network of distant metastasis of breast cancer and provide rationale for targeting the m6A machinery in the treatment of TNBC.

Different DNA Sequencing Using DNA Graphs: A Study

Natural genetic material may shed light on gene expression mechanisms and aid in the detection of genetic disorders. Single Nucleotide Polymorphism (SNP), small insertions and deletions (indels), and major chromosomal anomalies are all chromosomal abnormality-related disorders. As a result, several methods have been applied to analyze DNA sequences, which constitutes one of the most critical aspects of biological research. Thus, numerous mathematical and algorithmic contributions have been made to DNA analysis and computing. Cost minimization, deployment, and sensitivity analysis to many factors are all components of sequencing platforms built on a quantitative framework and their operating mechanisms. This study aims to investigate the role of DNA sequencing and its representation in the form of graphs in the analysis of different diseases by means of DNA sequencing.

Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders

Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the β-oxidation of fatty acids, and the Krebs's cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.

Epi-Decoder: Decoding the Local Proteome of a Genomic Locus by Massive Parallel Chromatin Immunoprecipitation Combined with DNA-Barcode Sequencing

The genome in a eukaryotic cell is packaged into chromatin and regulated by chromatin-binding and chromatin-modifying factors. Many of these factors and their complexes have been identified before, but how each genomic locus interacts with its surrounding proteins in the nucleus over time and in changing conditions remains poorly described. Measuring protein-DNA interactions at a specific locus in the genome is challenging and current techniques such as capture of a locus followed by mass spectrometry require high levels of enrichment. Epi-Decoder, a method developed in budding yeast, enables systematic decoding of the proteome of a single genomic locus of interest without the need for locus enrichment. Instead, Epi-Decoder uses massive parallel chromatin immunoprecipitation of tagged proteins combined with barcoding a genomic locus and counting of coimmunoprecipitated barcodes by DNA sequencing (TAG-ChIP-Barcode-Seq). In this scenario, DNA barcode counts serve as a quantitative readout for protein binding of each tagged protein to the barcoded locus. Epi-Decoder can be applied to determine the protein-DNA interactions at a wide range of genomic loci, such as coding genes, noncoding genes, and intergenic regions. Furthermore, Epi-Decoder provides the option to study protein-DNA interactions upon changing cellular and/or genetic conditions. In this protocol, we describe in detail how to construct Epi-Decoder libraries and how to perform an Epi-Decoder analysis.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Fingerprint construction through genotyping by sequencing for applied breeding in Brassica rapa

This study evaluated genotyping by sequencing (GBS) protocol for fingerprinting Brassica rapa and the data derived were more reliable than the re-sequencing data of B. rapa. Of the 10 enzyme solutions used to analyze the numbers of genotypes and single nucleotide polymorphisms (SNPs) in B. rapa, five solutions showed better results, namely: A (HaeIII, 450-500 bp), E (RsaI+HaeIII, 500-550 bp), F (RsaI+HaeIII, 500-600 bp), G (RsaI+HaeIII, 'All' fragment), and J (RsaI+EcoRV-HF®, 'All' fragment). The five enzyme solutions showed less than 40% similarity in different individuals from various samples, and 90% similarity in between two individuals from one sample. The E enzyme solution was most suitable for fingerprinting B. rapa revealing well-distributed SNPs in the whole genome. Of the 82 highly inbred lines and 18 F1 lines of B. rapa sequenced by GBS in E enzyme solution, known parents of 10 F1 lines were verified and male parents were discovered for 8 F1 lines that had only known female parents. This study provided a valuable method for screening parents for F1 lines in B. rapa for applied breeding through efficient evaluation of GBS with varied library construction strategies.

Genome reconstruction and haplotype phasing using chromosome conformation capture methodologies

Genomic analysis of individuals or organisms is predicated on the availability of high-quality reference and genotype information. With the rapidly dropping costs of high-throughput DNA sequencing, this is becoming readily available for diverse organisms and for increasingly large populations of individuals. Despite these advances, there are still aspects of genome sequencing that remain challenging for existing sequencing methods. This includes the generation of long-range contiguity during genome assembly, identification of structural variants in both germline and somatic tissues, the phasing of haplotypes in diploid organisms and the resolution of genome sequence for organisms derived from complex samples. These types of information are valuable for understanding the role of genome sequence and genetic variation on genome function, and numerous approaches have been developed to address them. Recently, chromosome conformation capture (3C) experiments, such as the Hi-C assay, have emerged as powerful tools to aid in these challenges for genome reconstruction. We will review the current use of Hi-C as a tool for aiding in genome sequencing, addressing the applications, strengths, limitations and potential future directions for the use of 3C data in genome analysis. We argue that unique features of Hi-C experiments make this data type a powerful tool to address challenges in genome sequencing, and that future integration of Hi-C data with alternative sequencing assays will facilitate the continuing revolution in genomic analysis and genome sequencing.

Determination of complete chromosomal haplotypes by bulk DNA sequencing

Haplotype phase represents the collective genetic variation between homologous chromosomes and is an essential feature of non-haploid genomes. Here we describe a computational strategy to reliably determine complete whole-chromosome haplotypes using a combination of bulk long-range sequencing and Hi-C sequencing. We demonstrate that this strategy can resolve the haplotypes of parental chromosomes in diploid human genomes with high precision (>99%) and completeness (>98%) and assemble the syntenic structure of rearranged chromosomes in aneuploid cancer genomes at base pair level resolution. Our work enables direct interrogation of chromosome-specific alterations and chromatin reorganization using bulk DNA sequencing.

The cp genome characterization of Adenium obesum: Gene content, repeat organization and phylogeny

Adenium obesum (Forssk.) Roem. & Schult. belonging to the family Apocynaceae, is remarkable for its horticultural and ornamental values, poisonous nature, and medicinal uses. In order to have understanding of cp genome characterization of highly valued medicinal plant, and the evolutionary and systematic relationships, the complete plastome / chloroplast (cp) genome of A. obesum was sequenced. The assembled cp genome of A. obesum was found to be 154,437 bp, with an overall GC content of 38.1%. A total of 127 unique coding genes were annotated including 96 protein-coding genes, 28 tRNA genes, and 3 rRNA genes. The repeat structures were found to comprise of only mononucleotide repeats. The SSR loci are compososed of only A/T bases. The phylogenetic analysis of cp genomes revealed its proximity with Nerium oleander.

Phylotranscriptomic analysis of Dillenia indica L. (Dilleniales, Dilleniaceae) and its systematics implication

The recent massive development in the next-generation sequencing platforms and bioinformatics tools including cloud based computing have proven extremely useful in understanding the deeper-level phylogenetic relationships of angiosperms. The present phylotranscriptomic analyses address the poorly known evolutionary relationships of the order Dilleniales to order of the other angiosperms using the minimum evolution method. The analyses revealed the nesting of the representative taxon of Dilleniales in the MPT but distinct from the representative of the order Santalales, Caryophyllales, Asterales, Cornales, Ericales, Lamiales, Saxifragales, Fabales, Malvales, Vitales and Berberidopsidales.

Measuring mutagenicity in ecotoxicology: A case study of Cd exposure in Chironomus riparius

Existing mutagenicity tests for metazoans lack the direct observation of enhanced germline mutation rates after exposure to anthropogenic substances, therefore being inefficient. Cadmium (Cd) is a metal described as a mutagen in mammalian cells and listed as a group 1 carcinogenic and mutagenic substance. But Cd mutagenesis mechanism is not yet clear. Therefore, in the present study, we propose a method coupling short-term mutation accumulation (MA) lines with subsequent whole genome sequencing (WGS) and a dedicated data analysis pipeline to investigate if chronic Cd exposure on Chironomus riparius can alter the rate at which de novo point mutations appear. Results show that Cd exposure did not affect the basal germline mutation rate nor the mutational spectrum in C. riparius, thereby arguing that exposed organisms might experience a range of other toxic effects before any mutagenic effect may occur. We show that it is possible to establish a practical and easily implemented pipeline to rapidly detect germ cell mutagens in a metazoan test organism. Furthermore, our data implicate that it is questionable to transfer mutagenicity assessments based on in vitro methods to complex metazoans.

Protein Purification Technologies

Protein Biotechnology is an exciting and fast- growing area of research, with numerous industrial applications. The growing demand for developing efficient and rapid protein purification methods is driving research and growth in this area. Advances and progress in the techniques and methods of protein purification have been such that one can reasonably expect that any protein of a given order of stability may be purified to currently acceptable standards of homogeneity. However, protein manufacturing cost remains extremely high, with downstream processing constituting a substantial proportion of the overall cost. Understanding of the methods and optimization of the experimental conditions have become critical to the manufacturing industry in order to minimize production costs while satisfying the quality as well as all regulatory requirements. New purification processes exploiting specific, effective and robust methods and chromatographic materials are expected to guide the future of the protein purification market.

Functionalized carbon nanotube electrodes for controlled DNA sequencing

In the last decade, solid-state nanopores/nanogaps have attracted significant attention in the rapid detection of DNA nucleotides. However, reducing the noise through controlled translocation of the DNA nucleobases is a central issue for the development of nanogap/nanopore-based DNA sequencing to achieve single-nucleobase resolution. Furthermore, the high reactivity of the graphene pores/gaps causes clogging of the pore/gap, leading to the blockage of the pores/gaps, sticking, and irreversible pore closure. To address the prospective of functionalization of the carbon nanostructure and for accomplishing this objective, herein, we have studied the performance of functionalized closed-end cap armchair carbon nanotube (CNT) nanogap-embedded electrodes, which can improve the coupling through non-bonding electrons and may provide the possibility of N/O-H⋯π interactions with the nucleotides, as single-stranded DNA is transmigrated across the electrode. We have investigated the effect of functionalizing the closed-end cap CNT (6,6) electrodes with purine (adenine, guanine) and pyrimidine (thymine, cytosine) molecules. Weak hydrogen bonds formed between the probe molecule and the target DNA nucleobase enhance the electronic coupling and temporarily stabilize the translocating nucleobase against the orientational fluctuations, which may reduce noise in the current signal during experimental measurements. The findings of our density functional theory and non-equilibrium Green's function-based study indicate that this modeled setup could allow DNA nucleotide sequencing with a better and reliable yield, giving current traces that differ by at least 1 order of current magnitude for all the four target nucleotides. Thus, we feel that the functionalized armchair CNT (6,6) nanogap-embedded electrodes may be utilized for controlled DNA sequencing.

Rumen Microbiota Distribution Analyzed by High-Throughput Sequencing After Oral Doxycycline Administration in Beef Cattle

The beef cattle rumen is a heterogenous microbial ecosystem that is necessary for the host to digest food and support growth. The importance of the rumen microbiota (RM) is also widely recognized for its critical roles in metabolism and immunity. The level of health is indicated by a dynamic RM distribution. We performed high-throughput sequencing of the bacterial 16S rRNA gene to compare microbial populations between rumens in beef cattle with or without doxycycline treatment to assess dynamic microbiotic shifts following antibiotic administration. The results of the operational taxonomic unit analysis and alpha and beta diversity calculations showed that doxycycline-treated beef cattle had lower species richness and bacterial diversity than those without doxycycline. Bacteroidetes was the predominant phylum in rumen samples without doxycycline, while Proteobacteria was the governing phylum in the presence of doxycycline. On the family level, the top three predominant populations in group qlqlwy (not treated with doxycycline) were Prevotellaceae, Lachnospiraceae, and Ruminococcaceae, compared to Xanthomonadaceae, Prevotellaceae, and Rikenellaceae in group qlhlwy (treated with doxycycline). At the genus level, the top predominant population in group qlqlwy was unidentified_Prevotellaceae. However, in group qlhlwy, the top predominant population was Stenotrophomonas. The results revealed significant RM differences in beef cattle with or without doxycycline. Oral doxycycline may induce RM composition differences, and bacterial richness may also influence corresponding changes that could guide antibiotic use in adult ruminants. This study is the first to assess microbiota distribution in beef cattle rumen after doxycycline administration.

Defeating the trypanosomatid trio: proteomics of the protozoan parasites causing neglected tropical diseases

Mass spectrometry-based proteomics enables accurate measurement of the modulations of proteins on a large scale upon perturbation and facilitates the understanding of the functional roles of proteins in biological systems. It is a particularly relevant methodology for studying Leishmania spp., Trypanosoma cruzi and Trypanosoma brucei, as the gene expression in these parasites is primarily regulated by posttranscriptional mechanisms. Large-scale proteomics studies have revealed a plethora of information regarding modulated proteins and their molecular interactions during various life processes of the protozoans, including stress adaptation, life cycle changes and interactions with the host. Important molecular processes within the parasite that regulate the activity and subcellular localisation of its proteins, including several co- and post-translational modifications, are also accurately captured by modern proteomics mass spectrometry techniques. Finally, in combination with synthetic chemistry, proteomic techniques facilitate unbiased profiling of targets and off-targets of pharmacologically active compounds in the parasites. This provides important data sets for their mechanism of action studies, thereby aiding drug development programmes.

Complex Network Characterization Using Graph Theory and Fractal Geometry: The Case Study of Lung Cancer DNA Sequences

This paper discusses an approach developed for exploiting the local elementary movements of evolution to study complex networks in terms of shared common embedding and, consequently, shared fractal properties. This approach can be useful for the analysis of lung cancer DNA sequences and their properties by using the concepts of graph theory and fractal geometry. The proposed method advances a renewed consideration of network complexity both on local and global scales. Several researchers have illustrated the advantages of fractal mathematics, as well as its applicability to lung cancer research. Nevertheless, many researchers and clinicians continue to be unaware of its potential. Therefore, this paper aims to examine the underlying assumptions of fractals and analyze the fractal dimension and related measurements for possible application to complex networks and, especially, to the lung cancer network. The strict relationship between the lung cancer network properties and the fractal dimension is proved. Results show that the fractal dimension decreases in the lung cancer network while the topological properties of the network increase in the lung cancer network. Finally, statistical and topological significance between the complexity of the network and lung cancer network is shown.

Cultivating DNA Sequencing Technology After the Human Genome Project

When the Human Genome Project was completed in 2003, automated Sanger DNA sequencing with fluorescent dye labels was the dominant technology. Several nascent alternative methods based on older ideas that had not been fully developed were the focus of technical researchers and companies. Funding agencies recognized the dynamic nature of technology development and that, beyond the Human Genome Project, there were growing opportunities to deploy DNA sequencing in biological research. Consequently, the National Human Genome Research Institute of the National Institutes of Health created a program-widely known as the Advanced Sequencing Technology Program-that stimulated all stages of development of new DNA sequencing methods, from innovation to advanced manufacturing and production testing, with the goal of reducing the cost of sequencing a human genome first to $100,000 and then to $1,000. The events of this period provide a powerful example of how judicious funding of academic and commercial partners can rapidly advance core technology developments that lead to profound advances across the scientific landscape.

Alternative Quantifications of Landscape Complementation to Model Gene Flow in Banded Longhorn Beetles [Typocerus v. velutinus (Olivier)]

Rapid progression of human socio-economic activities has altered the structure and function of natural landscapes. Species that rely on multiple, complementary habitat types (i.e., landscape complementation) to complete their life cycle may be especially at risk. However, such landscape complementation has received little attention in the context of landscape connectivity modeling. A previous study on flower longhorn beetles (Cerambycidae: Lepturinae) integrated landscape complementation into a continuous habitat suitability 'surface', which was then used to quantify landscape connectivity between pairs of sampling sites using gradient-surface metrics. This connectivity model was validated with molecular genetic data collected for the banded longhorn beetle (Typocerus v. velutinus) in Indiana, United States. However, this approach has not been compared to alternative models in a landscape genetics context. Here, we used a discrete land use/land cover map to calculate landscape metrics related to landscape complementation based on a patch mosaic model (PMM) as an alternative to the previously published, continuous habitat suitability model (HSM). We evaluated the HSM surface with gradient surface metrics (GSM) and with two resistance-based models (RBM) based on least cost path (LCP) and commute distance (CD), in addition to an isolation-by-distance (IBD) model based on Euclidean distance. We compared the ability of these competing models of connectivity to explain pairwise genetic distances (R ST) previously calculated from ten microsatellite genotypes of 454 beetles collected from 17 sites across Indiana, United States. Model selection with maximum likelihood population effects (MLPE) models found that GSM were most effective at explaining pairwise genetic distances as a proxy for gene flow across the landscape, followed by the landscape metrics calculated from the PMM, whereas the LCP model performed worse than both the CD and the isolation by distance model. We argue that the analysis of a continuous HSM with GSM might perform better because of their combined ability to effectively represent and quantify the continuous degree of landscape complementation (i.e., availability of complementary habitats in vicinity) found at and in-between sites, on which these beetles depend. Our findings may inform future studies that seek to model habitat connectivity in complex heterogeneous landscapes as natural habitats continue to become more fragmented in the Anthropocene.

Benchmarking the MinION: Evaluating long reads for microbial profiling

Nanopore based DNA-sequencing delivers long reads, thereby simplifying the decipherment of bacterial communities. Since its commercial appearance, this technology has been assigned several attributes, such as its error proneness, comparatively low cost, ease-of-use, and, most notably, aforementioned long reads. The technology as a whole is under continued development. As such, benchmarks are required to conceive, test and improve analysis protocols, including those related to the understanding of the composition of microbial communities. Here we present a dataset composed of twelve different prokaryotic species split into four samples differing by nucleic acid quantification technique to assess the specificity and sensitivity of the MinION nanopore sequencer in a blind study design. Taxonomic classification was performed by standard taxonomic sequence classification tools, namely Kraken, Kraken2 and Centrifuge directly on reads. This allowed taxonomic assignments of up to 99.27% on genus level and 92.78% on species level, enabling true-positive classification of strains down to 25,000 genomes per sample. Full genomic coverage is achieved for strains abundant as low as 250,000 genomes per sample under our experimental settings. In summary, we present an evaluation of nanopore sequence processing analysis with respect to microbial community composition. It provides an open protocol and the data may serve as basis for the development and benchmarking of future data processing pipelines.

Fabrication of Si Micropore and Graphene Nanohole Structures by Focused Ion Beam

A biosensor formed by a combination of silicon (Si) micropore and graphene nanohole technology is expected to act as a promising device structure to interrogate single molecule biopolymers, such as deoxyribonucleic acid (DNA). This paper reports a novel technique of using a focused ion beam (FIB) as a tool for direct fabrication of both conical-shaped micropore in Si₃N₄/Si and a nanohole in graphene to act as a fluidic channel and sensing membrane, respectively. The thinning of thick Si substrate down to 50 µm has been performed prior to a multi-step milling of the conical-shaped micropore with final pore size of 3 µm. A transfer of graphene onto the fabricated conical-shaped micropore with little or no defect was successfully achieved using a newly developed all-dry transfer method. A circular shape graphene nanohole with diameter of about 30 nm was successfully obtained at beam exposure time of 0.1 s. This study opens a breakthrough in fabricating an integrated graphene nanohole and conical-shaped Si micropore structure for biosensor applications.

Genomic Medicine-Progress, Pitfalls, and Promise

In the wake of the Human Genome Project (HGP), strong expectations were set for the timeline and impact of genomics on medicine-an anticipated transformation in the diagnosis, treatment, and prevention of disease. In this Perspective, we take stock of the nascent field of genomic medicine. In what areas, if any, is genomics delivering on this promise, or is the path to success clear? Where are we falling short, and why? What have been the unanticipated developments? Overall, we argue that the optimism surrounding the transformational potential of genomics on medicine remains justified, albeit with a considerably different form and timescale than originally projected. We also argue that the field needs to pivot back to basics, as understanding the entirety of the genotype-to-phenotype equation is a likely prerequisite for delivering on the full potential of the human genome to advance the human condition.

Container-based bioinformatics with Pachyderm

Motivation

Computational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well-defined way to orchestrate those processing stages and (iii) a data management layer that tracks data as it moves through the processing pipeline.

Results

Pachyderm is an open-source workflow system and data management framework that fulfils these needs by creating a data pipelining and data versioning layer on top of projects from the container ecosystem, having Kubernetes as the backbone for container orchestration. We adapted Pachyderm and demonstrated its attractive properties in bioinformatics. A Helm Chart was created so that researchers can use Pachyderm in multiple scenarios. The Pachyderm File System was extended to support block storage. A wrapper for initiating Pachyderm on cloud-agnostic virtual infrastructures was created. The benefits of Pachyderm are illustrated via a large metabolomics workflow, demonstrating that Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility and scalability.

Availability and implementation

Pachyderm is available from https://github.com/pachyderm/pachyderm. The Pachyderm Helm Chart is available from https://github.com/kubernetes/charts/tree/master/stable/pachyderm. Pachyderm is available out-of-the-box from the PhenoMeNal VRE (https://github.com/phnmnl/KubeNow-plugin) and general Kubernetes environments instantiated via KubeNow. The code of the workflow used for the analysis is available on GitHub (https://github.com/pharmbio/LC-MS-Pachyderm).

Supplementary information

Supplementary data are available at Bioinformatics online.

Development and Analytical Validation of a DNA Dual-Strand Approach for the US Food and Drug Administration-Approved Next-Generation Sequencing-Based Praxis Extended RAS Panel for Metastatic Colorectal Cancer Samples

A next-generation sequencing method was developed that can distinguish single-stranded modifications from low-frequency somatic mutations present on both strands of DNA in formalin-fixed paraffin-embedded colorectal cancer samples. We applied this method for analytical validation of the Praxis Extended RAS Panel, a US Food and Drug Administration-approved companion diagnostic for panitumumab, on the Illumina MiSeqDx platform. With the use of the TruSeq amplicon workflow, both strands of DNA from the starting material were interrogated independently. Mutations were reported only if found on both strands; artifacts usually present on only one strand would not be reported. A total of 56 mutations were targeted within the KRAS and NRAS genes. A minimum read depth of 1800× per amplicon is required per sample but averaged >30,000× at maximum multiplexing levels. Analytical validation studies were performed to determine the simultaneous detection of mutations on both strands, reproducibility, assay detection level, precision of the assay across various factors, and the impact of interfering substances. In conclusion, this assay can clearly distinguish single-stranded artifacts from low-frequency mutations. Furthermore, the assay is accurate, precise, and reproducible, can achieve consistent detection of a mutation at 5% mutation frequency, exhibits minimal impact from tested interfering substances, and can simultaneously detect 56 mutations in a single run using 10 samples plus controls.

Patient-centered care and genomic medicine: A qualitative provider study in the military health system

The diagnostic and predictive information produced by genomic sequencing may impact medical management, and it is critical that providers and institutions are able to use this information appropriately for patient care. Guided by the patient-centered care model, we investigated provider perspectives of patient, provider, and system-level factors that could influence the implementation of genomic medicine within the integrated healthcare system of the US Department of Defense (DOD). The purpose of this study was to explore patient-centered care elements related to the application of genomic sequencing in a military healthcare facility to understand the current capability and key gaps for patient-centered genomic medicine. Twenty DOD healthcare providers were interviewed regarding their past experiences and future expectations of genetics and genomics. These semi-structured interviews were recorded, transcribed and analyzed. All providers interviewed had some experience with genetics, but the level of experience varied greatly. Providers reported widely differing degrees of knowledge and confidence regarding genetics and about military-specific policies regarding genetics which varied by specialty. In addition, most providers stated that their department did not currently have the infrastructure to allow for the care of patients with secondary genetic findings, defined as genetic findings which are intentionally examined because of their importance to healthcare management, but are unrelated to the reason the individual underwent sequencing. This study reveals gaps in key elements of patient-centered care related to genomic medicine that may be helpful to address in future implementation efforts.

Excess primer degradation by Exo I improves the preparation of 3' cDNA ligation-based sequencing libraries

RNA sequencing library construction using single-stranded ligation of a DNA adapter to 3' ends of cDNAs often produces primer-adapter byproducts, which compete with cDNA-adapter ligation products during library amplification and, therefore, reduces the number of informative sequencing reads. We find that Escherichia coli Exo I digestion efficiently and selectively removes surplus reverse transcription primer and thereby reduces the primer-adapter product contamination in 3' cDNA ligation-based sequencing libraries, including small RNA libraries, which are typically similar in size to the primer-adapter products. We further demonstrate that Exo I treatment does not lead to trimming of the cDNA 3' end when duplexed with the RNA template. Exo I digestion is easy to perform and implement in other protocols and could facilitate a more widespread use of 3' cDNA ligation for sequencing-based applications.

PROBABILISTIC MODELING AND ANALYSIS OF DNA FRAGMENTATION

Deoxyribonucleic Acid (DNA) sequencing has become indispensable to the modern biological and medicine sciences. DNA fragmentation is a preliminary step in a dominant technique called shotgun sequencing that provides a time and cost effective strategy for the DNA sequencing. In this paper, we propose a probabilistic model for the random DNA fragmentation and derive an average number of fragments with the suitable length along with the probability of covering the entire DNA strand through the de novo assembly or the referenced-based mapping assembly. We formulate the coverage problem in terms of the probability of bond breaking between nucleotides and the number of DNA molecules participating in the fragmentation process, and provide insights into the optimal DNA fragmentation. We obtain the lower bound for the minimum number of suitable fragments required to reconstruct the DNA strand with the specified reliability. We evaluate the derived results with our DNA Fragmentation Tool which demonstrate, the validity of these results based on our model. Finally, we update our model with respect to the fragments’ size distribution of real data.

Comparing Pool-seq, Rapture, and GBS genotyping for inferring weak population structure: The American lobster (Homarus americanus) as a case study

Unraveling genetic population structure is challenging in species potentially characterized by large population size and high dispersal rates, often resulting in weak genetic differentiation. Genotyping a large number of samples can improve the detection of subtle genetic structure, but this may substantially increase sequencing cost and downstream bioinformatics computational time. To overcome this challenge, alternative, cost-effective sequencing approaches, namely Pool-seq and Rapture, have been developed. We empirically measured the power of resolution and congruence of these two methods in documenting weak population structure in nonmodel species with high gene flow comparatively to a conventional genotyping-by-sequencing (GBS) approach. For this, we used the American lobster (Homarus americanus) as a case study. First, we found that GBS, Rapture, and Pool-seq approaches gave similar allele frequency estimates (i.e., correlation coefficient over 0.90) and all three revealed the same weak pattern of population structure. Yet, Pool-seq data showed F ST estimates three to five times higher than GBS and Rapture, while the latter two methods returned similar F ST estimates, indicating that individual-based approaches provided more congruent results than Pool-seq. We conclude that despite higher costs, GBS and Rapture are more convenient approaches to use in the case of species exhibiting very weak differentiation. While both GBS and Rapture approaches provided similar results with regard to estimates of population genetic parameters, GBS remains more cost-effective in project involving a relatively small numbers of genotyped individuals (e.g., <1,000). Overall, this study illustrates the complexity of estimating genetic differentiation and other summary statistics in complex biological systems characterized by large population size and migration rates.

A theoretical analysis of single molecule protein sequencing via weak binding spectra

We propose and theoretically study an approach to massively parallel single molecule peptide sequencing, based on single molecule measurement of the kinetics of probe binding (Havranek, et al., 2013) to the N-termini of immobilized peptides. Unlike previous proposals, this method is robust to both weak and non-specific probe-target affinities, which we demonstrate by applying the method to a range of randomized affinity matrices consisting of relatively low-quality binders. This suggests a novel principle for proteomic measurement whereby highly non-optimized sets of low-affinity binders could be applicable for protein sequencing, thus shifting the burden of amino acid identification from biomolecular design to readout. Measurement of probe occupancy times, or of time-averaged fluorescence, should allow high-accuracy determination of N-terminal amino acid identity for realistic probe sets. The time-averaged fluorescence method scales well to weakly-binding probes with dissociation constants of tens or hundreds of micromolar, and bypasses photobleaching limitations associated with other fluorescence-based approaches to protein sequencing. We argue that this method could lead to an approach with single amino acid resolution and the ability to distinguish many canonical and modified amino acids, even using highly non-optimized probe sets. This readout method should expand the design space for single molecule peptide sequencing by removing constraints on the properties of the fluorescent binding probes.

The Importance of the Hedgehog Signaling Pathway in Tumorigenesis of Spinal and Cranial Chordoma

Chordomas is rare malignant bone tumors thought to arise from remnants of embryonic notochord along the spine, frequently at the skull base and sacrum. Although chordoma is slow growing tumors, while are extremely recurrent, and aggressive, as well as the rate of prognosis remains poorly. Radical surgery and high-dose radiation are the most used treatments. Currently, there is no effective chemotherapeutic standard for chordomas. The Hedgehog (HH) pathway adjusts various processes included in expansion and differentiation of tissues and organs throughout the fetus’s life, furthermore cell growth and differentiation in the adult organism, of the cell in an adult organism, in which acute anesthesia is involved in multiple cancers. To study the role of signaling the hedgehog in the base of the skull and sacrum chordomas, the expression of SHH and GLI-1 levels were detected immuno histochemically, Additionally, PTCH-1 and GLI-1 expressions were distinguished by in- Situ- hybridization. Based on the findings presented herein, it is likely that the HH signal cascade was revealed even in cranial, where consecoently spinal chordoma and their recurrences play an important role. Our staining exhibited a canonical, ligand- dependent and autocrine Hedgehog signaling in skull base and sacrum chordomas including relapse. Due to the high levels of SHH and GLI-1 expression in all investigated chordoma samples, the study suggests a possible autocrine ligand-dependent activation of the canonical HH signaling cascade. A paracrine or non-canonical pathway cannot be excluded. Our results suggest that Hedgehog-inhibitors, like SHH-, GLI- and SMO- inhibitors, might serve as a potential and effective target for the treatment of chordomas.

Nucleic Acid-Barcoding Technologies: Converting DNA Sequencing into a Broad-Spectrum Molecular Counter

The emergence of high-throughput DNA sequencing technologies sparked a revolution in the field of genomics that has rippled into many branches of the life and physical sciences. The remarkable sensitivity, specificity, throughput, and multiplexing capacity that are inherent to parallel DNA sequencing have since motivated its use as a broad-spectrum molecular counter. A key aspect of extrapolating DNA sequencing to non-traditional applications is the need to append nucleic-acid barcodes to entities of interest. In this review, we describe the chemical and biochemical approaches that have enabled nucleic-acid barcoding of proteinaceous and non-proteinaceous materials and provide examples of downstream technologies that have been made possible by DNA-encoded molecules. As commercially available high-throughput sequencers were first released less than 15 years ago, we believe related applications will continue to mature and close by proposing new frontiers to support this assertion.

Electronic Transport through DNA Nucleotides in Atomically Thin Phosphorene Electrodes for Rapid DNA Sequencing

Rapid progresses in developing the fast, low-cost, and reliable methods for DNA sequencing are envisaged for development of personalized medicine. In this respect, nanotechnology has paved the role for the development of advanced DNA sequencing techniques including sequencing with solid-state nanopores or nanogaps. Herein, we have explored the application of a black phosphorene based nanogap-device for DNA sequencing. Using density-functional-theory based non-equilibrium Green's function approach, we have computed transverse transmission and current-voltage ( I- V) characteristics of all the four DNA nucleotides (deoxy adenosine monophosphate, deoxy guanidine monophosphate, deoxy thymidine monophosphate, and deoxy cytosine monophosphate) as functions of applied bias voltages. We deduce that it is in principle; possible to differentiate between all the four nucleotides by three sequencing runs at distinct applied bias voltages, i.e., at 0.2, 1.4, and 1.6 V, where individual identification of all the four nucleotides may be possible. Hence, we believe our study might be helpful for experimentalist towards the development of a phosphorene based nanodevice for DNA sequencing to diagnose critical diseases.

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

Background

Noises and artifacts may arise in several steps of the next-generation sequencing (NGS) process. Recently, an NGS library preparation method called SMART, or Switching Mechanism At the 5′ end of the RNA Transcript, is introduced to prepare ChIP-seq (chromatin immunoprecipitation and deep sequencing) libraries from small amount of DNA material, using the DNA SMART ChIP-seq Kit. 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.

Results

Here, 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 these 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 significantly improved ChIP-seq peaks and results.

Conclusions

SMARTcleaner 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.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2577-4) contains supplementary material, which is available to authorized users.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

DNA as a digital information storage device: hope or hype?

The total digital information today amounts to 3.52 × 10²² bits globally, and at its consistent exponential rate of growth is expected to reach 3 × 10²⁴ bits by 2040. Data storage density of silicon chips is limited, and magnetic tapes used to maintain large-scale permanent archives begin to deteriorate within 20 years. Since silicon has limited data storage ability and serious limitations, such as human health hazards and environmental pollution, researchers across the world are intently searching for an appropriate alternative. Deoxyribonucleic acid (DNA) is an appealing option for such a purpose due to its endurance, a higher degree of compaction, and similarity to the sequential code of 0's and 1's as found in a computer. This emerging field of DNA as means of data storage has the potential to transform science fiction into reality, wherein a device that can fit in our palms can accommodate the information of the entire world, as latest research has revealed that just four grams of DNA could store the annual global digital information. DNA has all the properties to supersede the conventional hard disk, as it is capable of retaining ten times more data, has a thousandfold storage density, and consumes 10⁸ times less power to store a similar amount of data. Although DNA has an enormous potential as a data storage device of the future, multiple bottlenecks such as exorbitant costs, excruciatingly slow writing and reading mechanisms, and vulnerability to mutations or errors need to be resolved. In this review, we have critically analyzed the emergence of DNA as a molecular storage device for the future, its ability to address the future digital data crunch, potential challenges in achieving this objective, various current industrial initiatives, and major breakthroughs.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.