Hasil untuk "Computer applications to medicine. Medical informatics"

Giulia Cartocci, Alexandre Veyrié, Nicola Cavagnetto et al.

Abstract Explainability is crucial for establishing user trust in Artificial Intelligence (AI), particularly within safety-critical domains such as Air Traffic Management (ATM) and Air Traffic Control (ATC). This study empirically investigates the effects of Explainable AI (XAI), specifically HeatMap-based visual explanations, on cognitive workload, user acceptance, and intention to use AI-driven decision-support systems among Air Traffic Control Officers (ATCOs). Despite significant theoretical advancements in the broader XAI domain, empirical evidence addressing the specific impact of visual explanations on human-AI interactions in safety-critical environments like ATC remains limited. To address these critical gaps, an experimental comparison was conducted between explainable (HeatMap) and non-explainable (BlackBox) AI conditions, involving two user groups: expert and student ATCOs. Both objective neurophysiological measures (Electroencephalography) and subjective questionnaires were employed to capture comprehensive user responses. Key findings revealed that the presence of visual explanations significantly reduced cognitive workload and enhanced users’ willingness to adopt the AI system, regardless of participants’ level of expertise. However, explicit perceptions of AI’s impact on work performance were predominantly influenced by expertise, with less experienced controllers reporting a greater perceived impact than their expert counterparts. By combining objective neurometrics with subjective user assessments, this research advances methodological rigor in evaluating human-AI interactions and highlights the importance of tailored, user-centric explanations. These findings directly contribute to practical guidelines for designing cognitively compatible and trustworthy AI tools in ATC, providing nuanced insights for targeted training and deployment strategies based on user expertise.

Zhiqiang Huo, John Booth, Thomas Monks et al.

Abstract Critically ill children who require inter-hospital transfers to paediatric intensive care units are sicker than other admissions and have higher mortality rates. Current transport practice primarily relies on early clinical assessments within the initial hours of transport. Real-time mortality risk during transport is lacking due to the absence of data-driven assessment tools. Addressing this gap, our research introduces the PROMPT (Patient-centred Real-time Outcome monitoring and Mortality PredicTion), an explainable end-to-end machine learning pipeline to forecast 30-day mortality risks. The PROMPT integrates continuous time-series vital signs and medical records with episode-specific transport data to provide real-time mortality prediction. The results demonstrated that with PROMPT, both the random forest and logistic regression models achieved the best performance with AUROC 0.83 (95% CI: 0.79–0.86) and 0.81 (95% CI: 0.76–0.85), respectively. The proposed model has demonstrated proof-of-principle in predicting mortality risk in transported children and providing individual-level model interpretability during inter-hospital transports.

Donna Davis, Stephen Alexanian

A study of a community of people with disabilities in a virtual world sheds new light on an important issue of health literacy that has to date remained underreported in the current body of research. Participants revealed a community of individuals who are adults role-playing via child avatars as a coping and recovery mechanism for childhood trauma. One case follows the experience of a woman who role plays an adopted child of a caring adult while another attempts to recreate different ages of herself to unpack past trauma and find therapeutic healing. This phenomenon, as well as both its risks and opportunities, are examined with important considerations for the future of digital mental health support for people who have experienced abuse as children. Researchers, policy makers, and mental health professionals are encouraged to consider the role of social virtual worlds in the future of telemedicine for PTSD therapy.

Line Stien, Carolyn Clausen, Inna Feldman et al.

Mental health conditions are among the highest disease burden on society, affecting approximately 20% of children and adolescents at any point in time, with depression and anxiety being the leading causes of disability globally. To improve treatment outcomes, healthcare organizations turned to clinical decision support systems (CDSSs) that offer patient-specific diagnoses and recommendations. However, the economic impact of CDSS is limited, especially in child and adolescent mental health. This systematic literature review examined the economic impacts of CDSS implemented in mental health services. We planned to follow PRISMA reporting guidelines and found only one paper to describe health and economic outcomes. A randomized, controlled trial of 336 participants found that 60% of the intervention group and 32% of the control group achieved symptom reduction, i.e. a 50% decrease as per the Symptom Checklist-90-Revised (SCL-90-R), a method to evaluate psychological problems and identify symptoms. Analysis of the incremental cost-effectiveness ratio found that for every 1% of patients with a successful treatment result, it added €57 per year. There are not enough studies to draw conclusions about the cost-effectiveness in a mental health context. More studies on economic evaluations of the viability of CDSS within mental healthcare have the potential to contribute to patients and the larger society.

Yudong Liu, Jia Xu, Zecong Yu et al.

Abstract Clinical symptom‐based diagnosis and therapy play a crucial role in personalized medicine and drug discovery. The syndromes, distinctive groups of clinical symptoms summarized by traditional Chinese medicine (TCM) theories and clinical experiences, are used as the core diagnostic criteria and therapeutic guidance in TCM. However, there is still a lack of standardized data, information, and intrinsic molecular basis to help TCM syndromes better classify diseases and guide tailored medications. To address this problem, we built the first integrated web platform, SoFDA (http://www.tcmip.cn/Syndrome/front/), with a curated ontology of 319 TCM syndromes, 8045 diseases, and 1359 TCM herbal formulas and their relationships with genes, diseases, and formulas. This platform proposed an association measurement by calculating Jaccard/Cosine similarities between TCM syndromes and their related biomedical entities with case and control validations. On this basis, the SoFDA platform enables biomedical and pharmaceutical scientists to rank and filter the most promising associations for disease diagnosis and tailored interventions. Conversely, the targeted gene sets and symptom sets can also be associated with TCM syndromes, formulas, and diseases for function illustration. Notably, SoFDA explores the multi‐way associations among diseases, TCM syndromes, symptom genes, herbal formulas, drug targets, and pathways in heterogeneous biomedical networks with lots of customization. The protocol here implements all the analyses above using the SoFDA platform. Collectively, SoFDA may provide insights into the biological basis of disease‐specific TCM syndromes and the underlying molecular mechanisms, as well as a tailored treatment for single or multiple symptoms within a syndrome.

Rong Huang, Hongxiu Li, Reima Suomi et al.

BackgroundIntelligent physical robots based on artificial intelligence have been argued to bring about dramatic changes in health care services. Previous research has examined the use of intelligent physical robots in the health care context from different perspectives; however, an overview of the antecedents and consequences of intelligent physical robot use in health care is lacking in the literature. ObjectiveIn this paper, we aimed to provide an overview of the antecedents and consequences of intelligent physical robot use in health care and to propose potential agendas for future research through a systematic literature review. MethodsWe conducted a systematic literature review on intelligent physical robots in the health care field following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Literature searches were conducted in 5 databases (PubMed, Scopus, PsycINFO, Embase, and CINAHL) in May 2021, focusing on studies using intelligent physical robots for health care purposes. Subsequently, the quality of the included studies was assessed using the Mixed Methods Appraisal Tool. We performed an exploratory content analysis and synthesized the findings extracted from the included articles. ResultsA total of 94 research articles were included in the review. Intelligent physical robots, including mechanoid, humanoid, android, and animalistic robots, have been used in hospitals, nursing homes, mental health care centers, laboratories, and patients’ homes by both end customers and health care professionals. The antecedents for intelligent physical robot use are categorized into individual-, organization-, and robot-related factors. Intelligent physical robot use in the health care context leads to both non–health-related consequences (emotional outcomes, attitude and evaluation outcomes, and behavioral outcomes) and consequences for (physical, mental, and social) health promotion for individual users. Accordingly, an integrative framework was proposed to obtain an overview of the antecedents and consequences of intelligent physical robot use in the health care context. ConclusionsThis study contributes to the literature by summarizing current knowledge in the field of intelligent physical robot use in health care, by identifying the antecedents and the consequences of intelligent physical robot use, and by proposing potential future research agendas in the specific area based on the research findings in the literature and the identified knowledge gaps.

Shruti Jayakumar, Viknesh Sounderajah, Pasha Normahani et al.

Abstract Artificial intelligence (AI) centred diagnostic systems are increasingly recognised as robust solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in secondary research studies regarding these technologies in order to influence key clinical and policymaking decisions. It is therefore essential that these studies accurately appraise methodological quality and risk of bias within shortlisted trials and reports. In order to assess whether this critical step is performed, we undertook a meta-research study evaluating adherence to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool within AI diagnostic accuracy systematic reviews. A literature search was conducted on all studies published from 2000 to December 2020. Of 50 included reviews, 36 performed the quality assessment, of which 27 utilised the QUADAS-2 tool. Bias was reported across all four domains of QUADAS-2. Two hundred forty-three of 423 studies (57.5%) across all systematic reviews utilising QUADAS-2 reported a high or unclear risk of bias in the patient selection domain, 110 (26%) reported a high or unclear risk of bias in the index test domain, 121 (28.6%) in the reference standard domain and 157 (37.1%) in the flow and timing domain. This study demonstrates the incomplete uptake of quality assessment tools in reviews of AI-based diagnostic accuracy studies and highlights inconsistent reporting across all domains of quality assessment. Poor standards of reporting act as barriers to clinical implementation. The creation of an AI-specific extension for quality assessment tools of diagnostic accuracy AI studies may facilitate the safe translation of AI tools into clinical practice.

Jason D. Greenwood, Nathaniel Nielsen, Nathaniel E. Miller

A woman in her late fifties was admitted to the Family Medicine Inpatient Service directly from Rheumatology clinic for polyarticular pain and erythema with concern for infection. She was taking immunosuppressant medications for a history of multiple autoimmune diseases. Examination showed increasing erythema and tenderness on the upper and lower extremity joints. Histologic evaluation, surgical evaluation, and cultures were consistent with mycobacterium haemophilum infection. Mycobacterium haemophilum is an uncommon opportunistic infection that usually affects immunocompromised patients. The patient was treated with a multi-drug antibiotic regimen for several months due to drug resistance. Although this opportunistic infection is not common it should be considered in the differential of immunocompromised patients with skin and articular symptoms. Treatment outcomes are usually favorable if it caught earlier in the course.

Silva, João Figueira, Almeida, João Rafael, Matos, Sérgio

BackgroundElectronic health records store large amounts of patient clinical data. Despite efforts to structure patient data, clinical notes containing rich patient information remain stored as free text, greatly limiting its exploitation. This includes family history, which is highly relevant for applications such as diagnosis and prognosis. ObjectiveThis study aims to develop automatic strategies for annotating family history information in clinical notes, focusing not only on the extraction of relevant entities such as family members and disease mentions but also on the extraction of relations between the identified entities. MethodsThis study extends a previous contribution for the 2019 track on family history extraction from national natural language processing clinical challenges by improving a previously developed rule-based engine, using deep learning (DL) approaches for the extraction of entities from clinical notes, and combining both approaches in a hybrid end-to-end system capable of successfully extracting family member and observation entities and the relations between those entities. Furthermore, this study analyzes the impact of factors such as the use of external resources and different types of embeddings in the performance of DL models. ResultsThe approaches developed were evaluated in a first task regarding entity extraction and in a second task concerning relation extraction. The proposed DL approach improved observation extraction, obtaining F1 scores of 0.8688 and 0.7907 in the training and test sets, respectively. However, DL approaches have limitations in the extraction of family members. The rule-based engine was adjusted to have higher generalizing capability and achieved family member extraction F1 scores of 0.8823 and 0.8092 in the training and test sets, respectively. The resulting hybrid system obtained F1 scores of 0.8743 and 0.7979 in the training and test sets, respectively. For the second task, the original evaluator was adjusted to perform a more exact evaluation than the original one, and the hybrid system obtained F1 scores of 0.6480 and 0.5082 in the training and test sets, respectively. ConclusionsWe evaluated the impact of several factors on the performance of DL models, and we present an end-to-end system for extracting family history information from clinical notes, which can help in the structuring and reuse of this type of information. The final hybrid solution is provided in a publicly available code repository.

Ruizhe Yang, Ying Zhang, Weiju Han et al.

Abstract Background The demand for assessing health-related quality of life (HRQoL) in chronic otitis media (COM) is increasing globally. The currently available Chinese-language patient-reported outcome measurement (PROM) specific for COM includes merely a limited range of related symptoms and dimensions. Hence, in this study, we aim to translate, culturally adapt, and validate the Zurich Chronic Middle Ear Inventory (ZCMEI-21) in Chinese, to enable a comprehensive evaluation of the patients’ subjective health outcome in COM. Methods We sampled and surveyed 223 COM patients at three tertiary referral centers in China, using the Chinese translation of ZCMEI-21 (ZCMEI-21-Chn) and the EQ-5D questionnaire, a generic measure of HRQoL. Confirmatory factor analysis (CFA) was performed to investigate the structural model fit to the dataset. Cronbach’s α and test-retest reliability coefficient were calculated to establish reliability, and correlation was tested between ZCMEI-Chn scores and EQ-5D scores for convergent validity. Results A total of 208 adult patients with COM were included, with a mean age of 46 years (SD 14 years) and a male proportion of 41% (85/208). A modified bifactor model with ωH of 0.65 and ECV of 0.47 was found to fit the scale scores, indicating fair general factor saturation and multidimensionality of the instrument. ZCMEI-21-Chn demonstrated good reliability (Cronbach’s α = 0.88, test-retest reliability = 0.88). The total scores of ZCMEI-21-Chn had a moderate correlation with a question directly addressing HRQoL (r = 0.40, p < 0.001), EQ-5D descriptive system score (r = 0.57, p < 0.001), and EQ-5D visual analogous scale (r = 0.30, p < 0.001). Conclusions The ZCMEI-21-Chn is valid, reliable and culturally adapted to Chinese adult patients with COM. This study offers clinicians an efficient and comprehensive instrument to quantify COM patients’ self-reported health outcomes, which could facilitate the standardization of HRQoL data aggregation in COM on a global scale.

Qiang Ma, Shuailin Li, Zhiqiang Xu et al.

The data presented in this article are related to the research paper entitled “Changes in N supply pathways under different long-term fertilization regimes in Northeast China” [1]. Seasonal dynamics of soil NH4+–N, NO3−–N, soil microbial biomass nitrogen (N) and fixed NH4+ were provided on the basis of a 26-year long-term experiment, including six treatments: no fertilizer (CK), recycled manure (M), N and P fertilizers (NP), P and K fertilizers (PK), N, P and K fertilizers (NPK), and NPK fertilizers with recycled manure (NPKM). The presentation of potential N retention and supply through soil microbial biomass N and fixed NH4+ pools at different growth stages is helpful for comparing the effects of different N pools on soil N transformation and assessing synchronies between crop N demand and soil N supply through different N pools.

Yoonho Chung, Dana Allswede, Jean Addington et al.

In a recent machine learning study classifying “brain age” based on cross-sectional neuroanatomical data, clinical high-risk (CHR) individuals were observed to show deviation from the normal neuromaturational pattern, which in turn was predictive of greater risk of conversion to psychosis and a pattern of stably poor functional outcome. These effects were unique to cases who were between 12 and 17 years of age when their prodromal and psychotic symptoms began, suggesting that neuroanatomical deviance observable at the point of ascertainment of a CHR syndrome marks risk for an early onset form of psychosis. In the present study, we sought to clarify the pattern of neuroanatomical deviance linked to this “early onset” form of psychosis and whether this deviance is associated with poorer premorbid functioning. T1 MRI scans from 378 CHR individuals and 190 healthy controls (HC) from the North American Prodrome Longitudinal Study (NAPLS2) were analyzed. Widespread smaller cortical volume was observed among CHR individuals compared with HC at baseline evaluation, particularly among the younger group (i.e., those who were 12 to 17 years of age). Moreover, the younger CHR individuals who converted or presented worsened clinical symptoms at follow-up (within 2 years) exhibited smaller surface area in rostral anterior cingulate, lateral and medial prefrontal regions, and parahippocampal gyrus relative to the younger CHR individuals who remitted or presented a stable pattern of prodromal symptoms at follow-up. In turn, poorer premorbid functioning in childhood was associated with smaller surface area in medial orbitofrontal, lateral frontal, rostral anterior cingulate, precuneus, and temporal regions. Together with our prior report, these results are consistent with the view that neuroanatomical deviance manifesting in early adolescence marks vulnerability to a form of psychosis presenting with poor premorbid adjustment, an earlier age of onset (generally prior to the age of 18 years), and poor long-term outcome. Keywords: Magnetic resonance imaging, Clinical high risk, Psychosis, Brain development, Premorbid functioning, Schizophrenia

Christian La, Patricia Linortner, Jeffrey D. Bernstein et al.

Objective: Parkinson's disease (PD) episodic memory impairments are common; however, it is not known whether these impairments are due to hippocampal pathology. Hippocampal Lewy-bodies emerge by Braak stage 4, but are not uniformly distributed. For instance, hippocampal CA1 Lewy-body pathology has been specifically associated with pre-mortem episodic memory performance in demented patients. By contrast, the dentate gyrus (DG) is relatively free of Lewy-body pathology. In this study, we used ultra-high field 7-Tesla to measure hippocampal subfields in vivo and determine if these measures predict episodic memory impairment in PD during life. Methods: We studied 29 participants with PD (age 65.5 ± 7.8 years; disease duration 4.5 ± 3.0 years) and 8 matched-healthy controls (age 67.9 ± 6.8 years), who completed comprehensive neuropsychological testing and MRI. With 7-Tesla MRI, we used validated segmentation techniques to estimate CA1 stratum pyramidale (CA1-SP) and stratum radiatum lacunosum moleculare (CA1-SRLM) thickness, dentate gyrus/CA3 (DG/CA3) area, and whole hippocampus area. We used linear regression, which included imaging and clinical measures (age, duration, education, gender, and CSF), to determine the best predictors of episodic memory impairment in PD. Results: In our cohort, 62.1% of participants with PD had normal cognition, 27.6% had mild cognitive impairment, and 10.3% had dementia. Using 7-Tesla MRI, we found that smaller CA1-SP thickness was significantly associated with poorer immediate memory, delayed memory, and delayed cued memory; by contrast, whole hippocampus area, DG/CA3 area, and CA1-SRLM thickness did not significantly predict memory. Age-adjusted linear regression models revealed that CA1-SP predicted immediate memory (beta[standard error]10.895[4.215], p < .05), delayed memory (12.740[5.014], p < .05), and delayed cued memory (12.801[3.991], p < .05). In the fully-adjusted models, which included all five clinical measures as covariates, only CA1-SP remained a significant predictor of delayed cued memory (13.436[4.651], p < .05). Conclusions: In PD, we found hippocampal CA1-SP subfield thickness estimated on 7-Tesla MRI scans was the best predictor of episodic memory impairment, even when controlling for confounding clinical measures. Our results imply that ultra-high field imaging could be a sensitive measure to identify changes in hippocampal subfields and thus probe the neuroanatomical underpinnings of episodic memory impairments in patients with PD. Keywords: Parkinson's disease, Episodic memory, Cognitive impairment, MRI, Hippocampus, CA1, 7 Tesla

Eithan Kotkowski, Larry R. Price, P. Mickle Fox et al.

Purpose: The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network. Methods: To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models. Key findings: Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049). Significance: This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays. Keywords: Anatomic likelihood estimation, ALE, BrainMap, Functional covariance, Functional MRI, Gray matter density, Hippocampal network model, Hippocampus, Magnetic resonance imaging, MRI, Meta-analysis, Meta-analytic connectivity modeling, MACM, Structural covariance, Structural MRI, Voxel-based morphometry, VBM

Amrita Kumari Panda, Satpal Singh Bisht, Bodh Raj Kaushal et al.

Abstract Background Hot springs harbor rich bacterial diversity that could be the source of commercially important enzymes, antibiotics and many more products. Most of the hot springs present in Northeast of India are unexplored and their microbial diversity analysis could be of great interest to facilitate various industrial, agricultural and medicinal applications. The present study is an attempt to analyze the comprehensive bacterial diversity of Yumthang hot spring, Sikkim located at an altitude of 11, 800 ft. with a close proximity of Tibet 27° 47′ 30″ N 88° 42′ E using culture independent approach i.e. 16S rRNA gene amplicon metagenomic sequencing. Results The temperature and pH of the hot spring was recorded as 390–410 C and 8 respectively. Metagenome comprised of 1, 381,343 raw sequences with a sequence length of 151 bp and 55.62% G + C content. Metagenome sequence information is submitted at NCBI, SRA database under accession no. SRP057072. A total of 9, 95, 955 pre-processed reads were clustered into 1, 999 representative OTUs (operational taxonomical units) phylogenetically comprising of 17 bacterial phyla including unknown phylum indicating 99 families. Hot spring bacterial community is dominated by Proteobacteria (54.33%), Actinobacteria (32.19%), Firmicutes (6.03%), Bacteroidetes (2.87%) and unclassified bacteria (2.91%) respectively out of the total reads. Conclusions Several bacterial and archaeal sequences remained taxonomically unclassified, indicating potentially novel microorganisms in this hot spring ecosystem. Metagenomics of this habitat will facilitate identification of microorganisms possessing industrially relevant traits.

Carmine Guerriero

The law and the economy are deeply influenced by the legal tradition or origin, which is the bundle of institutions shaping lawmaking and dispute adjudication. The two principal legal traditions, common law and civil law, have been transplanted through colonization and occupation to the vast majority of the jurisdictions in the world by a group of European countries. Here, I illustrate a novel dataset recording the lawmaking institution employed by 155 of these jurisdictions at independence and in 2000 and four discretion-curbing adjudication institutions adopted by 99 of these “transplants” at the same two points in time. Contrary to the “legal origins” scholars׳ assumption, 25 transplants changed the transplanted lawmaking institution and 95 modified at least one of the transplanted lawmaking and adjudication rules. In “Endogenous Legal Traditions” (Guerriero, 2016a) [12], I document that these reforms are consistent with a model of the design of legal institutions by societies heterogeneous in their endowment of both the extent of cultural heterogeneity and the quality of the political process. In “Endogenous Legal Traditions and Economic Outcomes” (Guerriero, 2016b) [13] moreover, I show the relevance of considering legal evolution and the endogeneity between legal traditions and economics outcomes. The data illustrated here also include the proxies for the determinants of legal evolution I use in “Endogenous Legal Traditions” (Guerriero, 2016a) [12] and the novel measure of economic outcomes I employ in “Endogenous Legal Traditions and Economic Outcomes” (Guerriero, 2016b) [13].

C. Safran

Tahiyah Nou‐Shene, Vikramkumar Pudi, K. Sridharan et al.

Autonomous vehicles engaged in terrain exploration are typically equipped with a camera. The camera is subjected to vibration as the vehicle moves so that the videos captured require stabilisation to facilitate accurate interpretation by remote operators. Dedicated architectures for video stabilisation that offer high performance while consuming low area and power are desirable for this application. This study presents a pipelined very large‐scale integration architecture. It is based on exploiting the separability property of the two‐dimensional (2‐D) Sobel matrix and the 2‐D Gaussian filtering matrix to obtain an efficient corner point detection architecture. It also employs the coordinate rotation digital computer architecture for global motion vector calculation. The proposed architecture has been coded in Verilog and synthesised for a field programmable gate array (FPGA), which offers massive parallelism at fairly low power. The proposed architecture is shown to be highly area efficient. An FPGA‐based autonomous vehicle has been fabricated, and experiments with a camera mounted on the vehicle are presented and analysed.

Sankalp Khanna, Abdul Sattar, David Hansen

Sarver Aaron L, Erdman Jesse, Starr Tim et al.

<p>Abstract</p> <p>Background</p> <p>Next generation sequencing approaches applied to the analyses of transposon insertion junction fragments generated in high throughput forward genetic screens has created the need for clear informatics and statistical approaches to deal with the massive amount of data currently being generated. Previous approaches utilized to 1) map junction fragments within the genome and 2) identify Common Insertion Sites (CISs) within the genome are not practical due to the volume of data generated by current sequencing technologies. Previous approaches applied to this problem also required significant manual annotation.</p> <p>Results</p> <p>We describe Transposon Annotation Poisson Distribution Association Network Connectivity Environment (TAPDANCE) software, which automates the identification of CISs within transposon junction fragment insertion data. Starting with barcoded sequence data, the software identifies and trims sequences and maps putative genomic sequence to a reference genome using the bowtie short read mapper. Poisson distribution statistics are then applied to assess and rank genomic regions showing significant enrichment for transposon insertion. Novel methods of counting insertions are used to ensure that the results presented have the expected characteristics of informative CISs. A persistent mySQL database is generated and utilized to keep track of sequences, mappings and common insertion sites. Additionally, associations between phenotypes and CISs are also identified using Fisher’s exact test with multiple testing correction. In a case study using previously published data we show that the TAPDANCE software identifies CISs as previously described, prioritizes them based on p-value, allows holistic visualization of the data within genome browser software and identifies relationships present in the structure of the data.</p> <p>Conclusions</p> <p>The TAPDANCE process is fully automated, performs similarly to previous labor intensive approaches, provides consistent results at a wide range of sequence sampling depth, has the capability of handling extremely large datasets, enables meaningful comparison across datasets and enables large scale meta-analyses of junction fragment data. The TAPDANCE software will greatly enhance our ability to analyze these datasets in order to increase our understanding of the genetic basis of cancers.</p>