Life-Course Trajectories of Childless Women: Country-Specific or Universal?

While existing research has documented complexities in biographies of childless women, few studies to date have systematically examined the life-course pathways of the childless from a comparative, cross-country perspective. In this paper, we analyse biographies of childless women in four countries-Germany, Italy, Poland, and the United States-in order to investigate whether pathways into childlessness are country-specific or commonly shared across institutional, cultural, and geographical settings. Partnership, education, and employment histories are examined using sequence analysis with dynamic Hamming distance and cluster analysis. Discrepancy analysis indicates a country-effect in women's biographies although life-course patterns identified in each country share similarities. Overall, seven life-course trajectories have been identified, with the most numerous cluster comprising single, working women who completed their education at a relatively young age. The results highlight a marked variation in the life-courses of childless women. Put together, these findings provide descriptive evidence for both country-specificity and cross-country similarity in the pathways to childlessness.

NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides

Tumor homing peptides (THPs) play a crucial role in recognizing and specifically binding to cancer cells. Although experimental approaches can facilitate the precise identification of THPs, they are usually time-consuming, labor-intensive, and not cost-effective. However, computational approaches can identify THPs by utilizing sequence information alone, thus highlighting their great potential for large-scale identification of THPs. Herein, we propose NEPTUNE, a novel computational approach for the accurate and large-scale identification of THPs from sequence information. Specifically, we constructed variant baseline models from multiple feature encoding schemes coupled with six popular machine learning algorithms. Subsequently, we comprehensively assessed and investigated the effects of these baseline models on THP prediction. Finally, the probabilistic information generated by the optimal baseline models is fed into a support vector machine-based classifier to construct the final meta-predictor (NEPTUNE). Cross-validation and independent tests demonstrated that NEPTUNE achieved superior performance for THP prediction compared with its constituent baseline models and the existing methods. Moreover, we employed the powerful SHapley additive exPlanations method to improve the interpretation of NEPTUNE and elucidate the most important features for identifying THPs. Finally, we implemented an online web server using NEPTUNE, which is available at http://pmlabstack.pythonanywhere.com/NEPTUNE. NEPTUNE could be beneficial for the large-scale identification of unknown THP candidates for follow-up experimental validation.

MeShClust v3.0: high-quality clustering of DNA sequences using the mean shift algorithm and alignment-free identity scores

Background

Tools for accurately clustering biological sequences are among the most important tools in computational biology. Two pioneering tools for clustering sequences are CD-HIT and UCLUST, both of which are fast and consume reasonable amounts of memory; however, there is a big room for improvement in terms of cluster quality. Motivated by this opportunity for improving cluster quality, we applied the mean shift algorithm in MeShClust v1.0. The mean shift algorithm is an instance of unsupervised learning. Its strong theoretical foundation guarantees the convergence to the true cluster centers. Our implementation of the mean shift algorithm in MeShClust v1.0 was a step forward. In this work, we scale up the algorithm by adapting an out-of-core strategy while utilizing alignment-free identity scores in a new tool: MeShClust v3.0.

Results

We evaluated CD-HIT, MeShClust v1.0, MeShClust v3.0, and UCLUST on 22 synthetic sets and five real sets. These data sets were designed or selected for testing the tools in terms of scalability and different similarity levels among sequences comprising clusters. On the synthetic data sets, MeShClust v3.0 outperformed the related tools on all sets in terms of cluster quality. On two real data sets obtained from human microbiome and maize transposons, MeShClust v3.0 outperformed the related tools by wide margins, achieving 55%–300% improvement in cluster quality. On another set that includes degenerate viral sequences, MeShClust v3.0 came third. On two bacterial sets, MeShClust v3.0 was the only applicable tool because of the long sequences in these sets. MeShClust v3.0 requires more time and memory than the related tools; almost all personal computers at the time of this writing can accommodate such requirements. MeShClust v3.0 can estimate an important parameter that controls cluster membership with high accuracy.

Conclusions

These results demonstrate the high quality of clusters produced by MeShClust v3.0 and its ability to apply the mean shift algorithm to large data sets and long sequences. Because clustering tools are utilized in many studies, providing high-quality clusters will help with deriving accurate biological knowledge.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08619-0).

DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in the same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, Deep Regulatory Code and Tools for Alternative Polyadenylation (DeeReCT-APA), to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a convolutional neural network-long short-term memory (CNN-LSTM) architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo.

Socio-spatial stratification of housing tenure trajectories in Sweden - A longitudinal cohort study

Individuals tend to be most mobile when they are between 20 and 40 years of age. This pattern is relatively stable across regions and over time. For geographical mobility, less is known about their transitions between different types of housing and tenure forms. In Sweden, households may select between, principally, three different types of tenure forms, each often coupled with a specific housing type. Households may rent from either public companies (municipality owned) or private landlords in multifamily dwellings, households may own their single-family house privately, or they can cooperatively own a multifamily house as a tenant-owner in an apartment. Yet we lack knowledge of which tenure trajectories individuals tend to follow during their most mobile years, and we also lack knowledge about which factors determine tenure trajectories. Our sample consist of individuals who in 1995 were aged 18-25 and who left their parental house between 1994 and 1995. This study tracks their tenure trajectories for 21 consecutive years starting in 1995 until 2015. The cohorts in our sample were the first who encountered the conditions on the deregulated housing market that are still in place in Sweden today. We followed these cohorts until they were between 39 and 46 years old and used sequence analysis to classify tenure trajectories. One result that stands out is the outstanding and increasing emphasis on home ownership in our sample, quite unlike the traditional picture of the Swedish housing market. Additionally, we found that resources in a broad sense and spatial context have a great impact on the type of trajectory individuals follow.

Isolation and characterization of the novel bacteriophage vB_SmaS_BUCT626 against Stenotrophomonas maltophilia

Stenotrophomonas maltophilia has been recognized as an emerging global opportunistic pathogen, and it is intrinsically resistant to most antibiotics, which makes the limited choice for treating S. maltophilia infections. Bacteriophage with the proper characterization is considered as a promising alternative treatment option to control S. maltophilia infections. In this study, we isolated a novel Siphoviridae bacteriophage vB_SmaS_BUCT626 with lytic activity against S. maltophilia. Phage vB_SmaS_BUCT626 can lysis 10 of 20 S. maltophilia and was relatively stable at a wide range of temperatures (4-70 °C) and pH values (3.0-13.0) and exhibited good tolerance to chloroform. The genome of phage vB_SmaS_BUCT626 was a 61,662-bp linear double-stranded DNA molecule with a GC content of 56.2%, and contained 100 open-reading frames. It carried no antibiotic resistance, toxin, virulence-related genes, or lysogen-formation gene clusters. Together, these characteristics make phage vB_SmaS_BUCT626, a viable candidate as a biocontrol agent against S. maltophilia infection.

ARResT/Interrogate Immunoprofiling Platform: Concepts, Workflows, and Insights

ARResT/Interrogate was built within the EuroClonality-NGS working group to meet the challenge of developing and applying assays for the high-throughput sequence-based profiling of immunoglobulin (IG) and T-cell receptor (TR) repertoires. We herein present basic concepts, outline the main workflow, delve into EuroClonality-NGS-specific aspects, and share insights from our experiences with the platform.

Neighborhood deprivation and obesity: Sex-specific effects of cross-sectional, cumulative and residential trajectory indicators

Obesity is a long-term health issue that is becoming increasingly prevalent. Very few studies have considered the life course effects of neighborhood characteristics on obesity. In a sample of 35,856 adult participants (representative of the population of the Province of Quebec in Canada), we measured the association between neighborhood deprivation and obesity using logistic modelling on indicators of cross-sectional neighborhood deprivation, cumulative neighborhood deprivation and trajectories of neighborhood deprivation. For cross-sectional exposure, we found that females in our sample had higher odds of being affected by obesity when living in high-deprivation (OR 1.73, CI 1.41-2.13) or medium-deprivation neighborhoods (OR 1.27, CI 1.07-1.51) compared to females living in low-deprivation neighborhoods. Males also had higher odds of being affected by obesity when living in medium or high deprivation. For cumulative exposure to neighborhood deprivation, only females in the second highest category for longitudinal exposure to deprived neighborhoods had significantly higher odds of living with obesity (OR 1.89 CI 1.12-3.19) compared to females in the low cumulative exposure category. Using sequence analysis to determine neighborhood deprivation trajectories for up to 17 years, we found that females with a Deprived upward (OR 1.75 CI 1.10-2.78), an Average downward (OR 1.75 CI 1.08-2.84) or a Deprived trajectory (OR 1.81 CI 1.45-2.86) had higher odds of living with obesity compared to the Privileged trajectory. For males, there were no significant associations. Using trajectory indicators was beneficial to our analyses because this method shows that not only are individuals in low socioeconomic status neighborhoods at the end of their trajectory more susceptible to living with obesity, but so are those exposed to neighborhood deprivation at the beginning of their trajectory. These results could help to more precisely identify individuals at higher risk of developing obesity-related health issues.

Foot-related diabetes complications: care pathways, patient profiles and costs

BACKGROUND

Foot-related diabetes complications reduce individual well-being, increase mortality and results in increased healthcare costs. Despite their notable stress on health services, studies examining the foot complication care pathways, especially from the viewpoint of health services, are limited. We aimed to identify the most typical care pathways following an initial foot-related diabetes complication, to characterize the patients on each pathway and calculate the related healthcare costs.

METHODS

The identification of pathways was based on population-wide register-based data including all persons diagnosed with diabetes in Finland from 1964 to 2017. For each patient, initial foot-related complication from 2011-2016 was identified using the ICD-10 codes and related healthcare episodes were followed for two years until the end of 2017 or death. A sequence analysis was conducted on care episodes resulting in groups of typical care pathways, as well as their patient profiles. The costs of pathways resulting from the care episodes were calculated based on the data and the reported national unit costs and analyzed using linear models.

RESULTS

We identified six groups of typical pathways each comprising mainly single type of care episodes. Three of the groups comprised over 10 000 patients while the remaining groups ranged from a few hundred to a few thousand. Majority of pathways consisted only single care episode. However, among the rest of the care pathways variability in length of care pathways was observed between and within group of pathways. On average, the patients were over 65 years of age and were diagnosed with diabetes for over a decade. The pathways resulted in an annual cost of EUR 13 million. The mean costs were nearly 20-fold higher in the group with the highest costs (EUR 11 917) compared to the group with the lowest costs (EUR 609).

CONCLUSIONS

We identified groups of typical care pathways for diabetic foot and discovered notable heterogeneity in the resource use within the groups. This information is valuable in guiding the development of diabetes care to meet the growing need. Nevertheless, reasons underlying the observed heterogeneity requires further examination. Since foot complications are largely preventable, substantial savings could be achieved using cost-effective technologies and more efficient organization of care.

MALVIRUS: an integrated application for viral variant analysis

BACKGROUND

Being able to efficiently call variants from the increasing amount of sequencing data daily produced from multiple viral strains is of the utmost importance, as demonstrated during the COVID-19 pandemic, in order to track the spread of the viral strains across the globe.

RESULTS

We present MALVIRUS, an easy-to-install and easy-to-use application that assists users in multiple tasks required for the analysis of a viral population, such as the SARS-CoV-2. MALVIRUS allows to: (1) construct a variant catalog consisting in a set of variations (SNPs/indels) from the population sequences, (2) efficiently genotype and annotate variants of the catalog supported by a read sample, and (3) when the considered viral species is the SARS-CoV-2, assign the input sample to the most likely Pango lineages using the genotyped variations.

CONCLUSIONS

Tests on Illumina and Nanopore samples proved the efficiency and the effectiveness of MALVIRUS in analyzing SARS-CoV-2 strain samples with respect to publicly available data provided by NCBI and the more complete dataset provided by GISAID. A comparison with state-of-the-art tools showed that MALVIRUS is always more precise and often have a better recall.

Molecular detection and phylogenetic analysis of pigeon circovirus from racing pigeons in Northern China

Background

Pigeon circovirus (PiCV) infections in pigeons (Columba livia) have been reported worldwide. Currently, pigeon racing is becoming increasingly popular and considered to be a national sport in China, and even, the greatest competitions of racing pigeons are taking place in China. However, there are still no epidemiologic data regarding PiCV infections among racing pigeons in China. The purpose of our study was to provide information of prevalence, genetic variation and evolution of PiCV from racing pigeons in China.

Results

To trace the prevalence, genetic variation and evolution of PiCV in sick and healthy racing pigeons, 622 samples were collected from 11 provinces or municipalities in China from 2016 to 2019. The results showed that the positive rate of PiCV was 19.3% (120/622) at the sample level and 59.0% (23/39) at the club level, thus suggesting that the virus was prevalent in Chinese racing pigeons. A sequence analysis revealed that the cap genes of the PiCV strains identified in our study displayed a high genetic diversity and shared nucleotide homologies of 71.9%–100% and amino acid homologies of 71.7%–100%. 28 and 36 unique amino acid substitutions were observed in the Cap and Rep proteins derived from our PiCV strains, respectively. A cladogram representation of PiCV strains phylogeny based on 90 cap gene sequences showed that the strains in this study could be further divided into seven clades (A, B, C, E, G, H, and I) and some of them were closely related to worldwide strains from different types of pigeons. A large number of recombination events (31 events) were also detected in the PiCV genomes from Chinese racing pigeons.

Conclusions

These findings indicate that PiCV strains circulating in China exhibit a high genetic diversity and also contribute to information of prevalence, genetic variation and evolution of PiCV from racing pigeons in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08425-8.

Re-analysis of genomic data: An overview of the mechanisms and complexities of clinical adoption

Re-analyzing genomic information from a patient suspected of having an underlying genetic condition can improve the diagnostic yield of sequencing tests, potentially providing significant benefits to the patient and to the health care system. Although a significant number of studies have shown the clinical potential of re-analysis, less work has been performed to characterize the mechanisms responsible for driving the increases in diagnostic yield. Complexities surrounding re-analysis have also emerged. The terminology itself represents a challenge because "re-analysis" can refer to a range of different concepts. Other challenges include the increased workload that re-analysis demands of curators, adequate reimbursement pathways for clinical and diagnostic services, and the development of systems to handle large volumes of data. Re-analysis also raises ethical implications for patients and families, most notably when re-classification of a variant alters diagnosis, treatment, and prognosis. This review highlights the possibilities and complexities associated with the re-analysis of existing clinical genomic data. We propose a terminology that builds on the foundation presented in a recent statement from the American College of Medical Genetics and Genomics and describes each re-analysis process. We identify mechanisms for increasing diagnostic yield and provide perspectives on the range of challenges that must be addressed by health care systems and individual patients.

Biased expression of mutant alleles in cancer-related genes in esophageal squamous cell carcinoma

BACKGROUND

Recent progress of large-scale international studies has provided comprehensive catalogs of somatic mutations in cancers. Additionally, it has become evident that allelic imbalance in the abundance of somatic mutations between DNA and RNA were pervasive in various types of cancer. However, the allelic imbalance of the abundance of somatic mutations in esophageal squamous cell carcinoma (ESCC) has not been fully analyzed.

METHODS

We performed exome sequencing for 25 Japanese patients with ESCC to detect a comprehensive catalog of somatic mutations in ESCC. Additionally, we performed mRNA sequencing to evaluate the allelic imbalance of the identified somatic mutations at the transcriptional level by comparing the mutant allele frequencies between RNA and DNA.

RESULTS

The exome sequencing showed that TP53 and ZNF750 were significantly mutated genes. The expression levels of TP53 and ZNF750 were different depending on the mutation status. In almost all the tumors with missense mutations in TP53 and ZNF750, the mutant allele frequencies were higher in the RNA sequencing than those in the exome sequencing, indicating that the mutant alleles were preferentially expressed. By examining the allelic imbalances for all the identified missense mutations, we demonstrated that genes showing preferential expressions of the mutant alleles were involved in the pathways including cell cycle, cell death, and chromatin modification.

CONCLUSIONS

The results of this study suggest that the allelic imbalance of the abundance of somatic mutations plays important roles in the initiation and progression of ESCC by modulating cancer-related biological pathways.

miRMut: Annotation of mutations in miRNA genes from human whole-exome or whole-genome sequencing

Here, we present the miRMut protocol to annotate mutations found in miRNA genes based on whole-exome sequencing (WES) or whole-genome sequencing (WGS) results. The pipeline assigns mutation characteristics, including miRNA gene IDs (miRBase and MirGeneDB), mutation localization within the miRNA precursor structure, potential RNA-binding motif disruption, the ascription of mutation according to Human Genome Variation Society (HGVS) nomenclature, and miRNA gene characteristics, such as miRNA gene confidence and miRNA arm balance. The pipeline includes creating tabular and graphical summaries. For complete details on the use and execution of this protocol, please refer to Urbanek-Trzeciak et al. (2020).

Identifying patients and assessing variant pathogenicity for an autosomal dominant disease-driving gene

Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches. For complete details on the use and execution of this protocol, please refer to Gennarino et al. (2018).

Multi-CUT&Tag to simultaneously profile multiple chromatin factors

Genome-wide chromatin mapping approaches typically focus on one protein at a time. We recently developed multi-CUT&Tag, which enables simultaneous mapping of multiple chromatin proteins in the same single cells or pools of cells. Using barcoded adapters loaded onto antibody-protein A-Tn5 transposase complexes, multi-CUT&Tag marks the locations of each chromatin protein and directly detects colocalization of different proteins in the same cell(s). Although slightly more laborious than CUT&Tag, multi-CUT&Tag provides a powerful option for generating multi-factor maps for epigenomic profiling. For complete details on the use and execution of this protocol, please refer to Gopalan et al. (2021).

Mapping atypical UV photoproducts in vitro and across the S. cerevisiae genome

Exposure to ultraviolet (UV) light induces DNA damage, predominantly cyclobutane pyrimidine dimers (CPD) and 6,4-photoproducts (6,4-PP), as well as rare, atypical photoproducts at thymidine-adenine (TA) sequences. We have recently shown 'TA' photoproducts are induced in UV-irradiated oligonucleotides and across the budding yeast genome. Here, we describe a protocol for mapping atypical 'TA' photoproducts in vitro and in vivo. This protocol overcomes the technical challenges involved in accurately mapping such rare photoproducts by using ultraviolet damage endonuclease (UVDE) enzymes. For complete details on the use and execution of this protocol, please refer to Laughery et al. (2020).

Identification and pathogenicity of hepatitis E Virus from laboratory Bama miniature pigs

Background

Hepatitis E virus (HEV) genotypes 3 and 4 are zoonotic. In this study, HEV infection in laboratory Bama miniature pigs in Sichuan Province of China was investigated. Firstly, one hundred rectal swabs were collected for HEV RNA testing, and chose positive samples for sequence analysis. Concurrently, for pathogenicity study, six healthy Bama miniature pigs were randomly divided into two groups of 3 pigs each. A total of 500 μL of HEV stock (positive fecal samples identified in this study) was inoculated intravenously into each pig in the experimental group, and the three pigs in the other group served as negative controls. Serum and fecal samples were collected at 1 to 10 weeks post-inoculation (wpi) for alanine aminotransferase (ALT) levels, anti-HEV antibodies and HEV RNA detection, respectively. During necropsies, liver lesions and HEV antigen in liver were observed at 10 wpi.

Results

The rate of fecal sample HEV RNA-positivity was 12% (12/100). Sequence comparisons indicated that partial ORF1 and ORF2 gene sequences of this isolate shared highest identities with corresponding sequences of genotype 4a HEV isolates (81.4%-96.1% and 89.9%-97.1%, respectively). Phylogenetic tree analysis further demonstrated that sequences of this isolate clustered together with sub-genotype 4a HEV isolate sequences. Experimentally, the pathogenicity of Bama miniature pigs infected with this isolate exhibited viremia, fecal virus shedding, seroconversion, ALT level increasing, liver lesions and HEV antigen in liver.

Conclusions

This is the first study to confirm that HEV is currently circulating in laboratory Bama miniature pigs in China and this isolate can successfully infect Bama miniature pigs experimentally. More importantly, this study suggested HEV screening of laboratory pigs should be conducted to prevent research personnel from acquiring zoonotic HEV infections.

Identification of piRNA disease associations using deep learning

Piwi-interacting RNAs (piRNAs) play a pivotal role in maintaining genome integrity by repression of transposable elements, gene stability, and association with various disease progressions. Cost-efficient computational methods for the identification of piRNA disease associations promote the efficacy of disease-specific drug development. In this regard, we developed a simple, robust, and efficient deep learning method for identifying the piRNA disease associations known as piRDA. The proposed architecture extracts the most significant and abstract information from raw sequences represented in a simplicated piRNA disease pair without any involvement of features engineering. Two-step positive unlabeled learning and bootstrapping technique are utilized to abstain from the false-negative and biased predictions dealing with positive unlabeled data. The performance of proposed method piRDA is evaluated using k-fold cross-validation. The piRDA is significantly improved in all the performance evaluation measures for the identification of piRNA disease associations in comparison to state-of-the-art method. Moreover, it is thus projected conclusively that the proposed computational method could play a significant role as a supportive and practical tool for primitive disease mechanisms and pharmaceutical research such as in academia and drug design. Eventually, the proposed model can be accessed using publicly available and user-friendly web tool athttp://nsclbio.jbnu.ac.kr/tools/piRDA/.

Transformation, disruption or cumulative disadvantage? Labor market and education trajectories of young mothers in Australia

Young motherhood is often framed as detrimental to the life chances of young women with research showing negative impacts on education and labor market outcomes. At the same time, qualitative research reports narratives of motherhood as a transformative experience, providing motivation for a fresh start and moving young women away from previously unstable life pathways. These scenarios appear contradictory, however outcomes might vary for different groups of women depending on their pre-birth trajectories. We investigate the effects of early parenthood using the Household, Income and Labour Dynamics in Australia (HILDA) survey. We employ a sequence based approach to compare labor market- and educational precarity of young mothers and non-parenting peers. We employ a novel sequence matching technique creating a comparison group of non-parenting young women, based on similarities in early labor market trajectories. We find that young mothers have higher levels of precarity in their pre-birth trajectories. Moreover, our results show that becoming a young mother is connected to an average increase in labor market and educational precarity post birth, which supports the hypothesis of cumulative disadvantage. However, only mothers with the least precarious trajectories prior to birth experience this development, whereas young women already on highly precarious paths see a decrease in precarity over time. Although our results do not support cumulative disadvantage for the most disadvantaged women, neither does it support the idea of parenthood as a transformative event. Our results point to the importance of understanding heterogeneity in the outcomes of young mothers.

Seeing through the cat's eyes: evidence of a spontaneous perspective taking process using a non-human avatar

In many daily face-to-face interactions, people are able to take the perspective of others, for example, coding right and left based on point-of-view of others. In the present study, we investigated whether observers are able to take the perspective of a non-human figure such as a cat, observing the same effects obtained with human or robot avatars. In both experiments, we used a centrally presented stimulus (i.e. a cat), with its tail lateralized to the left or to the right. Participants had to respond to the side of the tail with a lateralized keypress. In Experiment 1 (spatial perspective taking task), participants were required to explicitly adopt the cat's perspective to respond, whereas in Experiment 2 (SR compatibility task), this was not explicitly required. In both experiments, faster RTs are obtained when the cat is presented back, with a greater difference between front and back views when the tail is on the right; furthermore, there is no temporal modulation of the back-front effect. These common results between the two experiments are interpreted on the basis of the spatial perspective taking processes, elicited voluntarily (Experiment 1) or spontaneously (Experiment 2).

From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome

Identification of genomic signals as indicators for functional genomic elements is one of the areas that received early and widespread application of machine learning methods. With time, the methods applied grew in variety and generally exhibited a tendency to improve their ability to identify some major genomic and transcriptomics signals. The evolution of machine learning in genomics followed a similar path to applications of machine learning in other fields. These were impacted in a major way by three dominant developments, namely an enormous increase in availability and quality of data, a significant increase in computational power available to machine learning applications, and finally, new machine learning paradigms, of which deep learning is the most well-known example. It is not easy in general to distinguish factors leading to improvements in results of applications of machine learning. This is even more so in the field of genomics, where the advent of next-generation sequencing and the increased ability to perform functional analysis of raw data have had a major effect on the applicability of machine learning in OMICS fields. In this paper, we survey the results from a subset of published work in application of machine learning in the recognition of genomic signals and regions in human genome and summarize some lessons learnt from this endeavor. There is no doubt that a significant progress has been made both in terms of accuracy and reliability of models. Questions remain however whether the progress has been sufficient and what these developments bring to the field of genomics in general and human genomics in particular. Improving usability, interpretability and accuracy of models remains an important open challenge for current and future research in application of machine learning and more generally of artificial intelligence methods in genomics.

Long undecoded transcript isoform (LUTI) detection in meiotic budding yeast by direct RNA and transcript leader sequencing

LUTIs (Long Undecoded Transcript Isoforms) are 5'-extended and poorly translated mRNAs that can downregulate transcription from promoters more proximal to a gene's coding sequence (CDS). In this protocol, polyA RNA is extracted from budding yeast cells undergoing highly synchronized meiosis. Using a combination of long-read direct RNA sequencing and transcript leader sequencing (TL-seq), meiosis-specific LUTIs are systematically identified. Following identification, TL-seq is used to quantify the abundance of both LUTI and the more canonical gene-proximal (PROX) transcripts. For complete details on the use and execution of this protocol, please refer to Tresenrider et al. (2021).

ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid-identifying barcodes on the chip. The end goal is that, over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over six consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing.

•

Designed ProtSeq protein sequencing method compatible with widely used NGS technology

•

Built Target-Switch SELEX to isolate aptamers specific to N-terminal amino acids (AAs)

•

Showed binding, ligation, cleavage, and NGS of six DNA binders in ordered barcode chain

•

Developed pipeline to deconvolve AAs from DNA barcodes to identify putative proteins

Genetic characterization of Chikungunya virus circulating in individuals from Paraná, Brazil

Phylogenetic analysis carried out in several Brazilian regions shows the circulation of the Asian and East-Central South African (ECSA) Chikungunya virus (CHIKV) genotypes in the country. Until now, there are no genetic studies about CHIKV strains circulating in the South region. In this study, we sequenced 5 new partial sequences of the CHIKV Envelope 1 gene from strains detected in Paraná state during the years 2016-2017. Maximum likelihood and neighbor-joining trees grouped all sequences in Brazilian branches within ECSA genotype and comparative analysis did not show E1-A226V mutation. However, we identified E1-K211T amino acid substitution in a sample demonstrating the dispersion of mutant strains in the country.

Malignant solitary fibrous tumor in the central nervous system treated with surgery, radiotherapy and anlotinib: A case report

BACKGROUND

Solitary fibrous tumor (SFT) of the central nervous system is rare. It is predominantly benign and rarely malignant. There is no established standardized treatment regimen for malignant intracranial SFTs.

CASE SUMMARY

We present a rare case of SFT in a 9-year-old girl with a space-occupying effect in the frontal-parietal lobes. She underwent craniotomy, and the mass was resected. Immunohistochemistry examination of the specimen showed that Ki-67 proliferation index staining was highly positive in 80% of tumor cells. Whole exome sequencing of the surgical tissue showed 38 somatic gene mutations and 1 gene amplification such as fibroblast growth factor receptor 4 or TP53. At 1.5 mo after surgery, head magnetic resonance imaging revealed that the tumor had recurred. The patient received 60 Gy and 30 fractions of intensity modulated radiotherapy. The patient then received anlotinib 8 mg po qd for 1-14 d of a 21 d cycle. Following this regimen, the patient achieved stable disease for > 17 mo. Magnetic resonance imaging at 1.5 year after surgery showed that the tumor had not progressed.

CONCLUSION

This is the first reported case of SFT of the central nervous system treated with surgery, radiotherapy and anlotinib. This regimen may be an effective treatment option for malignant intracranial SFT patients.

Evaluating the long-term impact of projected climate on rice-lentil-groundnut cropping system in Lower Gangetic Plain of India using crop simulation modelling

Most simulations of food production in response to various climates to date have used simulations of the same crop over multiple years. This study evaluated the impact of projected climate on performance of rice-lentil-groundnut cropping sequence in New Alluvial Zone of West Bengal, India, using DSSAT model. The study period consisted of baseline (1980-2010), mid-century (2040-2069) and end-century (2070-2099). Advancement in days to anthesis (2-13 days) was simulated for rice during the future periods. For lentil and groundnut, average advancement in days to anthesis was 1 day. Days to maturity were shortened by 3-16 days for rice and 0-7 days for lentil. Nevertheless, for groundnut, the days to maturity were simulated to increase by 1-9 days. The impact on final biomass and yield was simulated with and without CO₂ fertilization, and the positive impact of CO₂ fertilization was prominent for all the three crops. When CO₂ fertilization effect was considered, the yield of rice was projected to increase by 11-32%. On the other hand, yield of lentil and groundnut was estimated to change by - 31 to - 12% and - 33 to + 8%, respectively. Enhanced CO₂ could mitigate the magnitude of yield reduction due to enhanced temperature. Rice was benefited due to the carryover effect of residue from preceding groundnut and, hence, could sustain the yield on a long term. The study could also quantify the uncertainty in simulation of yield due to selection of GCMs.

Sequence similarity of HSP65 of Mycobacterium bovis BCG with SARS-CoV-2 spike and nuclear proteins: may it predict an antigen-dependent immune protection of BCG against COVID-19?

The Bacillus Calmette-Guérin (BCG) vaccine is known to have protective effects not only against tuberculosis but also against other unrelated infectious diseases caused by different pathogens. Several epidemiological studies have also documented the beneficial influence of BCG vaccine in reducing both susceptibility to and severity of SARS-CoV-2 infection. The protective, non-specific effects of BCG vaccination would be related to an antigen-independent enhancement of the innate immunity, termed trained immunity. However, the knowledge that heat shock protein (HSP)65 is the main antigen of Mycobacterium bovis BCG prompted us to verify whether sequence similarity existed between HSP65 and SARS-CoV-2 spike (S) and nuclear (N) proteins that could support an antigen-driven immune protection of BCG vaccine. The results of the in silico investigation showed an extensive sequence similarity of HSP65 with both the viral proteins, especially SARS-CoV-2 S, that also involved the regions comprising immunodominant epitopes. The finding that the predicted B cell and CD4+ T cell epitopes of HSP65 shared strong similarity with the predicted B and T cell epitopes of both SARS-CoV-2 S and N would support the possibility of a cross-immune reaction of HSP65 of BCG with SARS-CoV-2.

Identification, Quantification, and Testing of Alternative Splicing Events from RNA-Seq Data Using SplAdder

Alternative splicing (AS) is a regulatory process during mRNA maturation that shapes higher eukaryotes' complex transcriptomes. High-throughput sequencing of RNA (RNA-Seq) allows for measurements of AS transcripts at an unprecedented depth and diversity. The ever-expanding catalog of known AS events provides biological insights into gene regulation, population genetics, or in the context of disease. Here, we present an overview on the usage of SplAdder, a graph-based alternative splicing toolbox, which can integrate an arbitrarily large number of RNA-Seq alignments and a given annotation file to augment the shared annotation based on RNA-Seq evidence. The shared augmented annotation graph is then used to identify, quantify, and confirm alternative splicing events based on the RNA-Seq data. Splice graphs for individual alignments can also be tested for significant quantitative differences between other samples or groups of samples.

An overview of databases and bioinformatics tools for plant antimicrobial peptides

Antimicrobial peptides (AMPs) are small, ribosomally synthesized proteins found in nearly all forms of life. In plants, AMPs play a central role in plant defense due to their distinct physicochemical properties. Due to their broad-spectrum antimicrobial activity and rapid killing action, plant AMPs have become important candidates for the development of new drugs to control plant and animal pathogens that are resistant to multiple drugs. Further research is required to explore the potential uses of these natural compounds. Computational strategies have been increasingly used to understand key aspects of antimicrobial peptides. These strategies will help to minimize the time and cost of "wet-lab" experimentation. Researchers have developed various tools and databases to provide updated information on AMPs. However, despite the increased availability of antimicrobial peptide resources in biological databases, finding AMPs from plants can still be a difficult task. The number of plant AMP sequences in current databases is still small and yet often redundant. To facilitate further characterization of plant AMPs, we have summarized information on the location, distribution, and annotations of plant AMPs available in the most relevant databases for AMPs research. We also mapped and categorized the bioinformatics tools available in these databases. We expect that this will allow researchers to advance in the discovery and development of new plant AMPs with potent biological properties. We hope to provide insights to further expand the application of AMPs in the fields of biotechnology, pharmacy, and agriculture.

Multiplexed single-cell RNA-sequencing of mouse thymic and splenic samples

Multiplexed single-cell RNA-sequencing (scRNA-seq) enables investigating several biological samples in one scRNA-seq experiment. Here, we use antibodies tagged with a hashtag oligonucleotide (Ab-HTO) to label each sample, and 10× Genomics technology to analyze single-cell gene expression. Advantages of sample multiplexing are to reduce the cost of scRNA-seq assay and to avoid batch effect. It may also facilitate cell-doublet removal and the merging of several scRNA-seq assays. Herein, we apply multiplexed scRNA-seq to investigate mouse thymocytes and splenic T lymphocytes development. For complete details on the use and execution of this protocol, please refer to Nozais et al. (2021).

RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery

Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.

•

Efficient motif anchoring helps to get good quality contextual information on binding

•

Realistic and high granularity datasets ensure better performance of the classifiers

•

DNN models on the contextual features outperform more complex deep learning tools

•

RBPSpot algorithm may be used to develop RBP binding models for other species also

Identifying tumorigenic non-coding mutations through altered cis-regulation

Identification of non-coding mutations driving tumorigenesis requires alternative approaches to coding mutations. Enriched associations between mutated regulatory elements and altered cis-regulation in tumors are a promising approach to stratify candidate non-coding driver mutations. Here we provide a bioinformatics pipeline to mine data from the Cancer Genomic Commons (GDC) for such associations. The pipeline integrates RNA and whole-genome sequencing with genotyping data to reveal putative non-coding driver mutations by cancer type.

For complete information on the generation and use of this protocol, please refer to Cheng et al. (2021).

•

Integration of expression, genotyping, and whole-genome sequencing modalities

•

RNA-seq profiles resolved to gene-level allele-specific expression (ASE).

•

Mutated cis-regulatory features associated with gene-level ASE by cancer type

CloneSeq - Single-cell clonal 3D culture and analysis protocol

This CloneSeq protocol combines clonal expansion inside 3D hydrogel spheres and droplet-based RNA sequencing to resolve the limited sensitivity of single-cell approaches. CloneSeq can reveal rare subpopulations and support cellular stemness. CloneSeq can be adapted to different biological systems to discover rare subpopulations by leveraging clonal enhanced sensitivity. Important considerations include the hydrogel composition, adaptation of 3D cultured clones to the inDrops system, and inherent adhesive properties of the cells. CloneSeq is only validated for cell lines so far. For complete details on the use and execution of this protocol, please refer to (Bavli et al., 2021).

Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations

Bulk expression data from heterogeneous cell populations pose a challenge for investigators, as differences in cell numbers and transcriptional programs may complicate analysis. To improve the performance of bulk RNA sequencing on mixed populations, we created Immune Cell Linkage through Exploratory Matrices (ICLite). The ICLite package for R constructs modules of correlated genes and identifies their relationship to specific lineages in mixed cell populations. This protocol details formatting, optimization of run parameters, and interpretation of results following implementation of ICLite. For complete details on the use and execution of this protocol, please refer to Camiolo et al. (2021).

Generation of RNA sequencing libraries for transcriptome analysis of globin-rich tissues of the domestic dog

We have developed a protocol for barcoded cDNA libraries of 48 samples to study gene expression across tissues in the domestic dog, Canis familiaris, by modifying the Single-Cell Tagged Reverse Transcription (STRT) protocol (Islam et al., 2012, 2014). The cDNA reads represent mRNA 5′ ends, enabling the study of transcription start sites (TSS). Our modifications include longer UMIs for molecular counting and Globin-Lock® to deplete globin mRNAs that are abundant in blood and blood-rich tissues dominating all reads.

•

transcriptome analysis across tissues of domestic dog, Canis familiaris

•

RNA-seq library preparation for 48 tissue samples in parallel

•

depletion of abundant globin mRNAs from blood and blood-rich tissues

•

study of transcription start sites with cDNA reads from 5′end

Purification of astrocyte subtype based on a double reporter mice approach for downstream transcription profiling

Characterizing the molecular signature of a cell subtype leads to a better understanding of cell diversity, as this molecular data can identify new cellular markers and offer insights about cell function. Here, we describe an efficient protocol to separate a subtype of astrocytes, the Olig2-AS, from other glial cells by using a double reporter mouse approach and to determine the transcriptome profile of the Olig2-AS from the postnatal spinal cord using RNA-sequencing analysis. For complete details on the use and execution of this protocol, please refer to Ohayon et al. (2021).

Acquisition of murine splenic myeloid cells for protein and gene expression profiling by advanced flow cytometry and CITE-seq

Here, we outline detailed protocols to isolate and profile murine splenic dendritic cells (DCs) through advanced flow cytometry of the myeloid compartment and single-cell transcriptomic profiling with integrated cell surface protein expression through CITE-seq. This protocol provides a general transferrable road map for different tissues and species.

For complete details on the use and execution of this protocol, please refer to Lukowski et al. (2021).

•

Protocol to obtain integrated single-cell gene and protein expression data

•

Optimized flow cytometry panel for confident delineation of six main myeloid lineages

•

Gating strategy identifies large cell state heterogeneity within each lineage

Preparation of mouse pancreatic tumor for single-cell RNA sequencing and analysis of the data

Preparation of single-cell suspension from primary tumor tissue can provide a valuable resource for functional, genetic, proteomic, and tumor microenvironment studies. Here, we describe an effective protocol for mouse pancreatic tumor dissociation with further processing of tumor suspension for single-cell RNA sequencing analysis of cellular populations. We further provide an outline of the bioinformatics processing of the data and clustering of heterogeneous cellular populations comprising pancreatic tumors using Common Workflow Language (CWL) pipelines within user-friendly Scientific Data Analysis Platform (https://SciDAP.com).

For complete details on the use and execution of this protocol, please refer to Gabitova-Cornell et al. (2020).

•

Isolation of single-cell suspension from pancreatic tumor

•

Step-by-step guidance to the generation of quality single-cell RNA libraries

•

An online informatics pipeline SciDAP for downstream processes

•

SciDAP uses open source pipelines to obtain reproducible results

Identification of SARS-CoV-2 S RBD escape mutants using yeast screening and deep mutational scanning

Here, we describe a protocol to identify escape mutants on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) receptor-binding domain (RBD) using a yeast screen combined with deep mutational scanning. Over 90% of all potential single S RBD escape mutants can be identified for monoclonal antibodies that directly compete with angiotensin-converting enzyme 2 for binding. Six to 10 antibodies can be assessed in parallel. This approach has been shown to determine escape mutants that are consistent with more laborious SARS-CoV-2 pseudoneutralization assays.

For complete details on the use and execution of this protocol, please refer to Francino-Urdaniz et al. (2021).

•

A protocol for identifying S escape mutations for anti-S monoclonal antibodies

•

All anti-S antibodies that competitively inhibit ACE2 can be tested

•

Protocol uses yeast display screening of freely available S RBD libraries

•

Open-source software used to analyze sequencing data and identify escape mutants

Protocol for using NoBadWordsCombiner to merge and minimize "bad words" from BLAST hits against multiple eukaryotic gene annotation databases

Annotating protein-coding genes can be challenging, especially when searching for the best hits against multiple functional databases. This is partly because of "bad words" appearing as top hits, such as hypothetical or uncharacterized proteins. To help alleviate some of these issues, we designed a bioinformatics tool called NoBadWordsCombiner, which efficiently merges the hits from various databases, strengthening gene definitions by minimizing functional descriptions containing "bad words." Unlike other available tools, NoBadWordsCombiner is user friendly, but it does require users to have some general bioinformatics skills, including a basic understanding of the BLAST package and dash shell in Linux/Unix environments. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021a).

Protocol for design, construction, and selection of genome phage (gPhage) display libraries

This protocol describes the genomic phage (gPhage) display platform, a large-scale antigen and epitope mapping technique. We constructed a gPhage display peptide library of a eukaryotic organism, Trypanosoma cruzi (causative agent of Chagas disease), to map the antibody response landscape against the parasite. Here, we used an organism with a relatively large but intronless genome, although future applications could include other prevalent or (re)emerging infectious organisms carrying genomes with a limited number of introns. For complete details on the use and execution of this protocol, please refer to Teixeira et al. (2021).

Citizenship and education trajectories among children of immigrants: A transition-oriented sequence analysis

During recent decades, the educational outcomes of the children of immigrants have been extensively studied, with a growing emphasis on the heterogeneity of the so-called second generation. Yet, the impact of host country citizenship on children's educational outcomes has only received limited attention so far, although children of immigrants do not get automatic birthright citizenship in most European countries. Focusing on the Netherlands, this paper compares educational trajectories among citizen and non-citizen children of immigrants. Register data and sequence analysis are used to map and cluster the trajectories of a full cohort of second-generation students from the start of secondary school. We apply a variant of optimal matching focusing on sequences of transitions, which enables us to uncover different patterns of (im)mobility within a stratified school system better than the standard approach. Multinomial logistic regressions show that students who acquire Dutch citizenship are significantly more likely to follow upward trajectories, taking advantage of the system's flexibility and "back doors". Conversely, not having Dutch citizenship is associated with a higher risk of dropout and school interruptions. These findings are in line with our theoretical expectation that, during the naturalisation process, parents acquire or further develop important resources for navigating a complex educational system such as the Dutch one.

Integration of adeno-associated virus (AAV) into the genomes of most Thai and Mongolian liver cancer patients does not induce oncogenesis

BACKGROUND

Engineered versions of adeno-associated virus (AAV) are commonly used in gene therapy but evidence revealing a potential oncogenic role of natural AAV in hepatocellular carcinoma (HCC) has raised concerns. The frequency of potentially oncogenic integrations has been reported in only a few populations. AAV infection and host genome integration in another type of liver cancer, cholangiocarcinoma (CCA), has been studied only in one cohort. All reported oncogenic AAV integrations in HCC come from strains resembling the fully sequenced AAV2 and partly sequenced AAV13. When AAV integration occurs, only a fragment of the AAV genome is detectable in later DNA or RNA sequencing. The integrated fragment is typically from the 3' end of the AAV genome, and this positional bias has been only partly explained. Three research groups searched for evidence of AAV integration in HCC RNAseq samples in the Cancer Genome Atlas (TCGA) but reported conflicting results.

RESULTS

We collected and analyzed whole transcriptome and viral capture DNA sequencing in paired tumor and non-tumor samples from two liver cancer Asian cohorts from Thailand (N = 147, 47 HCC and 100 intrahepatic cholangiocarcinoma (iCCA)) and Mongolia (N = 70, all HCC). We found only one HCC patient with a potentially oncogenic integration of AAV, in contrast to higher frequency reported in European patients. There were no oncogenic AAV integrations in iCCA patients. AAV genomic segments are present preferentially in the non-tumor samples of Thai patients. By analyzing the AAV genome positions of oncogenic and non-oncogenic integrated fragments, we found that almost all the putative oncogenic integrations overlap the X gene, which is present and functional only in the strain AAV2 among all fully sequenced strains. This gene content difference could explain why putative oncogenic integrations from other AAV strains have not been reported. We resolved the discrepancies in previous analyses of AAV presence in TCGA HCC samples and extended it to CCA. There are 12 TCGA samples with an AAV segment and none are in Asian patients. AAV segments are present in preferentially in TCGA non-tumor samples, like what we observed in the Thai patients.

CONCLUSIONS

Our findings suggest a minimal AAV risk of hepatocarcinogenesis in Asian liver cancer patients. The partial genome presence and positional bias of AAV integrations into the human genome has complicated analysis of possible roles of AAV in liver cancer.

Chaos game representation and its applications in bioinformatics

Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.

The importance of adverse childhood experiences for labour market trajectories over the life course: a longitudinal study

BACKGROUND

Transitioning from school to work is important in influencing people's trajectories throughout their life course. This study investigated the extent to which adverse childhood experiences (ACEs) were associated with differences in labour market trajectories for young adults in the context of a Nordic child care regime with low levels of child poverty.

METHODS

Information on labour market participation, educational events, and public transfer records was recoded into seven state spaces for each month between ages 16 and 32 for a cohort of Danish adolescents born in a rural county in 1983 (N = 3373). Cluster analysis of the sequences using the optimal matching algorithm was used to identify groups with similar trajectories. Multinomial regression was used to assess the association between self-reported ACEs and cluster membership, taking gender and family of origin into account.

RESULTS

'In employment' was the state space in which the young adults spent the most time over their early life courses (mean: 85 out of 204 months; 42%). Cluster analysis identified three clusters. Cluster 3 was most distinct, where the mean time 'outside the labour market' was 149 months (73%), and only 17 months (8%) were spent 'in employment'. Cumulative ACEs increased the probability of being included in Cluster 3 (OR: 1.51). Experiencing parental divorce (OR: 3.05), witnessing a violent event (OR: 3.70), and being abused (OR: 5.64) were most strongly associated with Cluster 3 membership.

CONCLUSIONS

Labour market trajectories among adolescents with a higher number of ACEs consisted of more time outside the labour market, compared to adolescents who had experienced fewer adversities. The lasting consequences of childhood adversity should be taken more into account in welfare policies, even in countries such as Denmark, with high social security levels and high-quality universal childcare.

Hidden neural networks for transmembrane protein topology prediction

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.

In silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

The identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously, we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. In the simulation study presented here, we show that 97.1% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 69.4% can still be identified if the resolution is twice as low as currently attainable, and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof of concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future.

Fast parallel construction of variable-length Markov chains

BACKGROUND

Alignment-free methods are a popular approach for comparing biological sequences, including complete genomes. The methods range from probability distributions of sequence composition to first and higher-order Markov chains, where a k-th order Markov chain over DNA has [Formula: see text] formal parameters. To circumvent this exponential growth in parameters, variable-length Markov chains (VLMCs) have gained popularity for applications in molecular biology and other areas. VLMCs adapt the depth depending on sequence context and thus curtail excesses in the number of parameters. The scarcity of available fast, or even parallel software tools, prompted the development of a parallel implementation using lazy suffix trees and a hash-based alternative.

RESULTS

An extensive evaluation was performed on genomes ranging from 12Mbp to 22Gbp. Relevant learning parameters were chosen guided by the Bayesian Information Criterion (BIC) to avoid over-fitting. Our implementation greatly improves upon the state-of-the-art even in serial execution. It exhibits very good parallel scaling with speed-ups for long sequences close to the optimum indicated by Amdahl's law of 3 for 4 threads and about 6 for 16 threads, respectively.

CONCLUSIONS

Our parallel implementation released as open-source under the GPLv3 license provides a practically useful alternative to the state-of-the-art which allows the construction of VLMCs even for very large genomes significantly faster than previously possible. Additionally, our parameter selection based on BIC gives guidance to end-users comparing genomes.

Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments

BACKGROUND

The use of sound to represent sequence data-sonification-has great potential as an alternative and complement to visual representation, exploiting features of human psychoacoustic intuitions to convey nuance more effectively. We have created five parameter-mapping sonification algorithms that aim to improve knowledge discovery from protein sequences and small protein multiple sequence alignments. For two of these algorithms, we investigated their effectiveness at conveying information. To do this we focussed on subjective assessments of user experience. This entailed a focus group session and survey research by questionnaire of individuals engaged in bioinformatics research.

RESULTS

For single protein sequences, the success of our sonifications for conveying features was supported by both the survey and focus group findings. For protein multiple sequence alignments, there was limited evidence that the sonifications successfully conveyed information. Additional work is required to identify effective algorithms to render multiple sequence alignment sonification useful to researchers. Feedback from both our survey and focus groups suggests future directions for sonification of multiple alignments: animated visualisation indicating the column in the multiple alignment as the sonification progresses, user control of sequence navigation, and customisation of the sound parameters.

CONCLUSIONS

Sonification approaches undertaken in this work have shown some success in conveying information from protein sequence data. Feedback points out future directions to build on the sonification approaches outlined in this paper. The effectiveness assessment process implemented in this work proved useful, giving detailed feedback and key approaches for improvement based on end-user input. The uptake of similar user experience focussed effectiveness assessments could also help with other areas of bioinformatics, for example in visualisation.