The information retrieval challenge of human digital memories

Today people are storing increasing amounts of personal information in digital format. While storage of such information is becoming straight forward, retrieval from the vast personal archives that this is creating poses significant challenges. Existing retrieval techniques are good at retrieving from non-personal spaces, such as the World Wide Web. However they are not sufficient for retrieval of items from these new unstructured spaces which contain items that are personal to the individual, and of which the user has personal memories and with which has had previous interaction. We believe that there are new and exciting possibilities for retrieval from personal archives. Memory cues act as triggers for individuals in the remembering process, a better understanding of memory cues will enable us to design new and effective retrieval algorithms and systems for personal archives. Context data, such as time and location, is already proving to play a key part in this special retrieval domain, for example for searching personal photo archives, we believe there are many other rich sources of context that can be exploited for retrieval from personal archives

Venturing into the labyrinth: the information retrieval challenge of human digital memories

Advances in digital capture and storage technologies mean that it is now possible to capture and store one’s entire life experiences in a Human Digital Memory (HDM). However, these vast personal archives are of little benefit if an individual cannot locate and retrieve significant items from them. While potentially offering exciting opportunities to support a user in their activities by providing access to information stored from previous experiences, we believe that the features of HDM datasets present new research challenges for information retrieval which must be addressed if these possibilities are to be realised. Specifically we postulate that effective retrieval from HDMs must exploit the rich sources of context data which can be captured and associated with items stored within them. User’s memories of experiences stored within their memory archive will often be linked to these context features. We suggest how such contextual metadata can be exploited within the retrieval process

The use of provenance in information retrieval

The volume of electronic information that users accumulate is steadily rising. A recent study [2] found that there were on average 32,000 pieces of information (e-mails, web pages, documents, etc.) for each user. The problem of organizin

차세대 시퀀싱 데이터에 대한 SNV/InDel 호출 및 하플로타이핑의 새로운 접근 방법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 박근수.차세대 시퀀싱 데이터에 대한 수많은 변이 호출 알고리즘이 개발되어 왔다. 대다수 변이 호출 알고리즘은 신뢰할 수 있지만 추가적인 성능 향상을 위한 여지는 남아있다. 특히, 낮은 리드 깊이를 가지는 데이터에 대한 변이 호출과 체세포 변이 호출은 개선될 여지가 많다. 첫 번째로, 본 논문에서는 위 양성 변이를 제거하여 변이 호출의 정밀도를 개선하는 새로운 알고리즘인 RDscan을 제안한다. RDscan은 잘못 정렬된 리드를 제거하고 리드를 재배치 한 후, 리드 깊이 분포에 기반한 변이의 신뢰도 점수를 계산하여 위 양성 변이를 제거한다. 우리는 최신 변이 호출 알고리즘들을 사용하여 RDscan의 성능을 평가하였다. 1000 게놈 프로젝트와 일루미나의 데이터세트에 대하여 RDscan을 통한 추가적인 변이 필터링은 테스트에 사용된 대부분의 변이 호출 알고리즘의 정확성을 개선시켰다. 생식세포 변이에 대한 호출은 12건의 테스트 중 11건, 체세포 변이 대한 호출은 24건의 테스트 중 21건에서 정확성이 증가되었다. 알려진 골드 스탠다드 변이 세트를 사용하여 각 알고리즘의 파라미터 최적화를 통해 생성된 최적의 변이 세트에 대해서도, RDscan 은 생식세포 변이에 대한 12건 중 5건, 체세포 변이에 대한 24 건 중 21건에서 변이 호출 정확성을 개선하였다. 임상 및 연구에서는 단일 게놈 가닥에 존재하는 변이의 세트 정보 (하플로타이핑)를 필요로 하는 경우가 있다. 특히 인간 백혈구 항원 유전자들에 대한 하플로타이핑은 실제 임상에서 다루는 중요한 문제이다. 기존 연구들은 차세대 시퀀싱 기반 알고리즘이 인간 백혈구 항원 유전자에 대한 하플로타이핑을 수행하기에 적합함을 보여주었다. 하지만, 하플로타이핑의 정확성을 저하시키는 대립 유전자의 상 조정 문제를 해결한 알고리즘은 없다. 두 번째로, 본 논문에서는 차세대 시퀀싱 데이터로부터 인간 백혈구 항원 유전자들에 대한 하플로타이핑을 수행하는 새로운 알고리즘인 HLAscan을 소개한다. HLAscan은 ImMunoGeneTics 프로젝트에서 제공하는 IMGT/HLA 데이터베이스의 인간 백혈구 항원 유전자 서열들에 대해 개인의 유전체 리드를 정렬한다. 그 후, 정렬된 리드의 분포에 기반한 점수를 사용하여 올바른 대립유전자의 상을 결정할 수 있다. HLAscan을 통한 하플로타이핑은1000 게놈 프로젝트와 HapMap 프로젝트의 공식 데이터세트에 대해서 기존의 차세대 시퀀싱 기반 알고리즘들보다 높은 정확성을 보여주었다. 또한 HiSeq X-TEN으로 생성한 아홉 가족의 데이터세트에 대해서, HLAscan을 사용한 하플로타이핑 결과는 96.9%의 정확성을 보였고, 그 중 90× 이상의 높은 리드 깊이를 가지는 데이터세트에 대해서는 100% 정확성을 보였다.Several tools have been developed for calling variants from next-generation sequencing data. Although they are generally accurate and reliable, most of them have room for improvement, especially in regard to calling variants in datasets with low read depth coverage. In addition, the somatic variants predicted by several somatic variant callers tend to have very low concordance rates. First, we propose a new tool (RDscan) for improving germline and somatic variant calling in next-generation sequencing data. RDscan removes misaligned reads, repositions reads, and calculates RDscore based on the read depth distribution. With RDscore, RDscan improves the precision of variant callers by removing false variants. When we tested our new tool using the latest variant calling algorithms, accuracy was improved for most of the algorithms. After screening variants with RDscan, calling accuracies increased for germline variants in 11 out of 12 cases and for somatic variants in 21 out of 24 cases. For the best set of variants produced by optimizing the parameters of each algorithm using the known truth sets, RDscan increased the calling accuracies for germline variants in 5 out of 12 cases and for somatic variants in 21 out of 24 cases. Some applications require information on multiple variants in a single genome strand (haplotyping). In particular, precise haplotyping for human leukocyte antigen genes is of great clinical importance. Several recent studies showed that next-generation sequencing based method is a feasible and promising technique for haplotyping of human leukocyte antigen genes. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. Second, we developed a new method (HLAscan) for HLA haplotyping using NGS data. HLAscan performs alignment of reads to HLA sequences from human leukocyte antigen (IMGT/HLA) database in the international ImMunoGeneTics project. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, -B, -C, -DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%.Abstract i Contents iii List of Figures iv List of Tables vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Problem Statement 7 1.3 Previous Works and New Results 8 1.4 Organization 10 Chapter 2 SNV and InDel Calling 11 2.1 Preliminaries 11 2.2 Germline Variant Calling Algorithm 16 2.3 Somatic Variant Calling Algorithm 21 2.4 Results 22 2.5 Discussions 46 Chapter 3 Haplotyping for MHC region 48 3.1 Preliminaries 48 3.2 Haplotyping Algorithm 52 3.3 Results 58 3.4 Discussions 72 Chapter 4 Conclusion 74 4.1 Summary 74 4.2 Future Directions 76 Bibliography 78Docto

This Time It's Personal: from PIM to the Perfect Digital Assistant

Interacting with digital PIM tools like calendars, to-do lists, address books, bookmarks and so on, is a highly manual, often repetitive and frequently tedious process. Despite increases in memory and processor power over the past two decades of personal computing, not much has changed in the way we engage with such applications. We must still manually decompose frequently performed tasks into multiple smaller, data specific processes if we want to be able to recall or reuse the information in some meaningful way. "Meeting with Yves at 5 in Stata about blah" breaks down into rigid, fixed semantics in separate applications: data to be recorded in calendar fields, address book fields and, as for the blah, something that does not necessarily exist as a PIM application data structure. We argue that a reason Personal Information Management tools may be so manual, and so effectively fragmented, is that they are not personal enough. If our information systems were more personal, that is, if they knew in a manner similar to the way a personal assistant would know us and support us, then our tools would be more helpful: an assistive PIM tool would gather together the necessary material in support of our meeting with Yves. We, therefore, have been investigating the possible paths towards PIM tools as tools that work for us, rather than tools that seemingly make us work for them. To that end, in the following sections we consider how we may develop a framework for PIM tools as "perfect digital assistants" (PDA). Our impetus has been to explore how, by considering the affordances of a Real World personal assistant, we can conceptualize a design framework, and from there a development program for a digital simulacrum of such an assistant that is not for some far off future, but for the much nearer term

Workshop on evaluating personal search

The first ECIR workshop on Evaluating Personal Search was held on 18th April 2011 in Dublin, Ireland. The workshop consisted of 6 oral paper presentations and several discussion sessions. This report presents an overview of the scope and contents of the workshop and outlines the major outcomes

HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

BACKGROUND: The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. RESULTS: We created HLAProfiler ( https://github.com/ExpressionAnalysis/HLAProfiler ), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. CONCLUSIONS: HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/

The role of places and spaces in lifelog retrieval

Finding relevant interesting items when searching or browsing within a large multi-modal personal lifelog archive is a significant challenge. The use of contextual cues to filter the collection and aid in the determination of relevant content is often suggested as means to address such challenges. This work presents an exploration of the various locations, garnered through context logging, several participants engaged in during personal information access over a 15 month period. We investigate the implications of the varying data accessed across multiple locations for context-based retrieval from such collections. Our analysis highlights that a large number of spaces and places may be used for information access, but high volume of content is accessed in few