Showing papers by "Johns Hopkins University published in 2022"

A complete reference genome improves analysis of human genetic variation

Abstract: Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 million base pairs of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome for clinical and functional study. We show how this reference universally improves read mapping and variant calling for 3202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of variants per sample in previously unresolved regions, showcasing the promise of the T2T-CHM13 reference for evolutionary and biomedical discovery. Simultaneously, this reference eliminates tens of thousands of spurious variants per sample, including reduction of false positives in 269 medically relevant genes by up to a factor of 12. Because of these improvements in variant discovery coupled with population and functional genomic resources, T2T-CHM13 is positioned to replace GRCh38 as the prevailing reference for human genetics.

Next-generation sequencing: insights to advance clinical investigations of the microbiome

Abstract: Next-generation sequencing (NGS) technology has advanced our understanding of the human microbiome by allowing for the discovery and characterization of unculturable microbes with prediction of their function. Key NGS methods include 16S rRNA gene sequencing, shotgun metagenomic sequencing, and RNA sequencing. The choice of which NGS methodology to pursue for a given purpose is often unclear for clinicians and researchers. In this Review, we describe the fundamentals of NGS, with a focus on 16S rRNA and shotgun metagenomic sequencing. We also discuss pros and cons of each methodology as well as important concepts in data variability, study design, and clinical metadata collection. We further present examples of how NGS studies of the human microbiome have advanced our understanding of human disease pathophysiology across diverse clinical contexts, including the development of diagnostics and therapeutics. Finally, we share insights as to how NGS might further be integrated into and advance microbiome research and clinical care in the coming years.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines

Abstract: Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

Improved transcriptome assembly using a hybrid of long and short reads with StringTie

Abstract: Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana , Mus musculus , and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie .

Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

Abstract: Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .

Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

Abstract: Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users and the knowledge imbalance between those users and ML designers. To investigate the state of transparent ML in medical image analysis, we conducted a systematic review of the literature from 2012 to 2021 in PubMed, EMBASE, and Compendex databases. We identified 2508 records and 68 articles met the inclusion criteria. Current techniques in transparent ML are dominated by computational feasibility and barely consider end users, e.g. clinical stakeholders. Despite the different roles and knowledge of ML developers and end users, no study reported formative user research to inform the design and development of transparent ML models. Only a few studies validated transparency claims through empirical user evaluations. These shortcomings put contemporary research on transparent ML at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research, we introduce the INTRPRT guideline, a design directive for transparent ML systems in medical image analysis. The INTRPRT guideline suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements. Following these guidelines increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.

Messenger RNA vaccines for cancer immunotherapy: progress promotes promise

Abstract: The COVID-19 pandemic has elevated mRNA vaccines to global recognition due to their unprecedented success rate in protecting against a deadly virus. This international success is underscored by the remarkable versatility, favorable immunogenicity, and overall safety of the mRNA platform in diverse populations. Although mRNA vaccines have been studied in preclinical models and patients with cancer for almost three decades, development has been slow. The recent technological advances responsible for the COVID-19 vaccines have potential implications for successfully adapting this vaccine platform for cancer therapeutics. Here we discuss the lessons learned along with the chemical, biologic, and immunologic adaptations needed to optimize mRNA technology to successfully treat cancers.

Modified Activated Carbon for Copper Ion Removal from Aqueous Solution

Abstract: Because of increasing environmental awareness, it is becoming more important to remove harmful elements from water solutions. This study used activated carbon (AC) derived from waste wood-based panels as the base material, oxidized with nitric acid (OAC), and grafted with iminodiacetic acid (IDA-OAC) to improve the adsorption capacity and affinity for metals. The characterization of AC, OAC, and IDA-OAC was conducted via FTIR, SEM, N2 adsorption and desorption analysis, elemental analysis, Boehm titration, and point of zero charge (PZC). The instrument studies proved the modified increasing of the functional groups of the adsorbents. Moreover, batch and column experiments were conducted to evaluate the ability of the three adsorbents to remove copper ions from aqueous solution. In batch sorption, IDA-OAC had the highest adsorption capacity (84.51 mg/g) compared to OAC (54.74 mg/g) and AC (24.86 mg/g) at pH 5. The breakthrough point (Ct/Ci = 0.05) of copper ions for IDA-OAC occurred much later than AC in the column experiment (AC = 19 BV, IDA-OAC = 52 BV). The Langmuir isotherm and pseudo-second-model kinetics modeling could better fit with the data obtained from the batch sorption of AC, OAC, and IDA-OAC. The significant capacity and reusability of IDA-OAC displayed high applicability for water treatment.

Immunity to Invasive Fungal Diseases

Abstract: Invasive fungal diseases are rare in individuals with intact immunity. This, together with the fact that there are only a few species that account for most mycotic diseases, implies a remarkable natural resistance to pathogenic fungi. Mammalian immunity to fungi rests on two pillars, powerful immune mechanisms and elevated temperatures that create a thermal restriction zone for most fungal species. Conditions associated with increased susceptibility generally reflect major disturbances of immune function involving both the cellular and humoral innate and adaptive arms, which implies considerable redundancy in host defense mechanisms against fungi. In general, tissue fungal invasion is controlled through either neutrophil or granulomatous inflammation, depending on the fungal species. Neutrophils are critical against Candida spp. and Aspergillus spp. while macrophages are essential for controlling mycoses due to Cryptococcus spp., Histoplasma spp., and other fungi. The increasing number of immunocompromised patients together with climate change could significantly increase the prevalence of fungal diseases.

Expanding the Photonic Palette: Exploring High Index Materials

Abstract: While the photonic community is occupied with exotic concepts portending a grand future and fame if not fortune, I respectfully entertain the possibility that a humble concept of simply increasing the refractive index by a modest factor may have a far greater payoff in many walks of life. With that in mind, I explore why higher index materials have not yet materialized and point out a few tentative directions for the search of these elusive materials, be they natural or artificial.