Haplotype-aware sequence alignment to pangenome graphs.

Modern pangenome graphs are built using haplotype-resolved genome assemblies. When mapping reads to a pangenome graph, prioritizing alignments that are consistent with the known haplotypes improves genotyping accuracy. However, the existing rigorous formulations for co-linear chaining and alignment problems do not consider the haplotype paths in a pangenome graph. This often leads to spurious read alignments to those paths that are unlikely recombinations of the known haplotypes. In this paper, we develop novel formulations and algorithms for sequence-to-graph alignment and chaining problems. Inspired by the genotype imputation models, we assume that a query sequence is an imperfect mosaic of reference haplotypes. Accordingly, we introduce a recombination penalty in the scoring functions for each haplotype switch. First, we solve haplotype-aware sequence-to-graph alignment in O(|Q||E||H|) time, where Q is the query sequence, E is the set of edges, and H is the set of haplotypes represented in the graph. To complement our solution, we prove that an algorithm significantly faster than O(|Q||E||H|) is impossible under the Strong Exponential Time Hypothesis (SETH). Second, we propose a haplotype-aware chaining algorithm that runs in O(|H|N log|H|N) time after graph preprocessing, where N is the count of input anchors. We then establish that a chaining algorithm significantly faster than O(|H|N) is impossible under SETH. As a proof-of-concept, we implemented our chaining algorithm in the Minichain aligner. By aligning sequences sampled from the human major histocompatibility complex (MHC) to a pangenome graph of 60 MHC haplotypes, we demonstrate that our algorithm achieves better consistency with ground-truth recombinations when compared to a haplotype-agnostic algorithm.

Inceptor counteracts insulin signalling in ß-cells to control glycaemia.

Resistance to insulin and insulin-like growth factor 1 (IGF1) in pancreatic ß-cells causes overt diabetes in mice; thus, therapies that sensitize ß-cells to insulin may protect patients with diabetes against ß-cell failure1-3. Here we identify an inhibitor of insulin receptor (INSR) and IGF1 receptor (IGF1R) signalling in mouse ß-cells, which we name the insulin inhibitory receptor (inceptor; encoded by the gene Iir). Inceptor contains an extracellular cysteine-rich domain with similarities to INSR and IGF1R4, and a mannose 6-phosphate receptor domain that is also found in the IGF2 receptor (IGF2R)5. Knockout mice that lack inceptor (Iir-/-) exhibit signs of hyperinsulinaemia and hypoglycaemia, and die within a few hours of birth. Molecular and cellular analyses of embryonic and postnatal pancreases from Iir-/- mice showed an increase in the activation of INSR-IGF1R in Iir-/- pancreatic tissue, resulting in an increase in the proliferation and mass of ß-cells. Similarly, inducible ß-cell-specific Iir-/- knockout in adult mice and in ex vivo islets led to an increase in the activation of INSR-IGF1R and increased proliferation of ß-cells, resulting in improved glucose tolerance in vivo. Mechanistically, inceptor interacts with INSR-IGF1R to facilitate clathrin-mediated endocytosis for receptor desensitization. Blocking this physical interaction using monoclonal antibodies against the extracellular domain of inceptor resulted in the retention of inceptor and INSR at the plasma membrane to sustain the activation of INSR-IGF1R in ß-cells. Together, our findings show that inceptor shields insulin-producing ß-cells from constitutive pathway activation, and identify inceptor as a potential molecular target for INSR-IGF1R sensitization and diabetes therapy.

The structure, function and evolution of a complete human chromosome 8.

The complete assembly of each human chromosome is essential for understanding human biology and evolution1,2. Here we use complementary long-read sequencing technologies to complete the linear assembly of human chromosome 8. Our assembly resolves the sequence of five previously long-standing gaps, including a 2.08-Mb centromeric α-satellite array, a 644-kb copy number polymorphism in the ß-defensin gene cluster that is important for disease risk, and an 863-kb variable number tandem repeat at chromosome 8q21.2 that can function as a neocentromere. We show that the centromeric α-satellite array is generally methylated except for a 73-kb hypomethylated region of diverse higher-order α-satellites enriched with CENP-A nucleosomes, consistent with the location of the kinetochore. In addition, we confirm the overall organization and methylation pattern of the centromere in a diploid human genome. Using a dual long-read sequencing approach, we complete high-quality draft assemblies of the orthologous centromere from chromosome 8 in chimpanzee, orangutan and macaque to reconstruct its evolutionary history. Comparative and phylogenetic analyses show that the higher-order α-satellite structure evolved in the great ape ancestor with a layered symmetry, in which more ancient higher-order repeats locate peripherally to monomeric α-satellites. We estimate that the mutation rate of centromeric satellite DNA is accelerated by more than 2.2-fold compared to the unique portions of the genome, and this acceleration extends into the flanking sequence.

Long-read mapping to repetitive reference sequences using Winnowmap2.

Approximately 5-10% of the human genome remains inaccessible due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. We show that existing long-read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a nonreference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy. To address this limitation, we developed a new long-read mapping method, Winnowmap2, by using minimal confidently alignable substrings. Winnowmap2 computes each read mapping through a collection of confident subalignments. This approach is more tolerant of structural variation and more sensitive to paralog-specific variants within repeats. Our experiments highlight that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences.

Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies.

Advances in long-read sequencing technologies and genome assembly methods have enabled the recent completion of the first telomere-to-telomere human genome assembly, which resolves complex segmental duplications and large tandem repeats, including centromeric satellite arrays in a complete hydatidiform mole (CHM13). Although derived from highly accurate sequences, evaluation revealed evidence of small errors and structural misassemblies in the initial draft assembly. To correct these errors, we designed a new repeat-aware polishing strategy that made accurate assembly corrections in large repeats without overcorrection, ultimately fixing 51% of the existing errors and improving the assembly quality value from 70.2 to 73.9 measured from PacBio high-fidelity and Illumina k-mers. By comparing our results to standard automated polishing tools, we outline common polishing errors and offer practical suggestions for genome projects with limited resources. We also show how sequencing biases in both high-fidelity and Oxford Nanopore Technologies reads cause signature assembly errors that can be corrected with a diverse panel of sequencing technologies.

Coverage-preserving sparsification of overlap graphs for long-read assembly.

MOTIVATION: Read-overlap-based graph data structures play a central role in computing de novo genome assembly. Most long-read assemblers use Myers's string graph model to sparsify overlap graphs. Graph sparsification improves assembly contiguity by removing spurious and redundant connections. However, a graph model must be coverage-preserving, i.e. it must ensure that there exist walks in the graph that spell all chromosomes, given sufficient sequencing coverage. This property becomes even more important for diploid genomes, polyploid genomes, and metagenomes where there is a risk of losing haplotype-specific information. RESULTS: We develop a novel theoretical framework under which the coverage-preserving properties of a graph model can be analyzed. We first prove that de Bruijn graph and overlap graph models are guaranteed to be coverage-preserving. We next show that the standard string graph model lacks this guarantee. The latter result is consistent with prior work suggesting that removal of contained reads, i.e. the reads that are substrings of other reads, can lead to coverage gaps during string graph construction. Our experiments done using simulated long reads from HG002 human diploid genome show that 50 coverage gaps are introduced on average by ignoring contained reads from nanopore datasets. To remedy this, we propose practical heuristics that are well-supported by our theoretical results and are useful to decide which contained reads should be retained to avoid coverage gaps. Our method retains a small fraction of contained reads (1-2%) and closes majority of the coverage gaps. AVAILABILITY AND IMPLEMENTATION: Source code is available through GitHub (https://github.com/at-cg/ContainX) and Zenodo with doi: 10.5281/zenodo.7687543.

A variant selection framework for genome graphs.

MOTIVATION: Variation graph representations are projected to either replace or supplement conventional single genome references due to their ability to capture population genetic diversity and reduce reference bias. Vast catalogues of genetic variants for many species now exist, and it is natural to ask which among these are crucial to circumvent reference bias during read mapping. RESULTS: In this work, we propose a novel mathematical framework for variant selection, by casting it in terms of minimizing variation graph size subject to preserving paths of length α with at most δ differences. This framework leads to a rich set of problems based on the types of variants [e.g. single nucleotide polymorphisms (SNPs), indels or structural variants (SVs)], and whether the goal is to minimize the number of positions at which variants are listed or to minimize the total number of variants listed. We classify the computational complexity of these problems and provide efficient algorithms along with their software implementation when feasible. We empirically evaluate the magnitude of graph reduction achieved in human chromosome variation graphs using multiple α and δ parameter values corresponding to short and long-read resequencing characteristics. When our algorithm is run with parameter settings amenable to long-read mapping (α = 10 kbp, δ = 1000), 99.99% SNPs and 73% SVs can be safely excluded from human chromosome 1 variation graph. The graph size reduction can benefit downstream pan-genome analysis. AVAILABILITY AND IMPLEMENTATION: : https://github.com/AT-CG/VF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Real-time mapping of nanopore raw signals.

MOTIVATION: Oxford Nanopore Technologies sequencing devices support adaptive sequencing, in which undesired reads can be ejected from a pore in real time. This feature allows targeted sequencing aided by computational methods for mapping partial reads, rather than complex library preparation protocols. However, existing mapping methods either require a computationally expensive base-calling procedure before using aligners to map partial reads or work well only on small genomes. RESULTS: In this work, we present a new streaming method that can map nanopore raw signals for real-time selective sequencing. Rather than converting read signals to bases, we propose to convert reference genomes to signals and fully operate in the signal space. Our method features a new way to index reference genomes using k-d trees, a novel seed selection strategy and a seed chaining algorithm tailored toward the current signal characteristics. We implemented the method as a tool Sigmap. Then we evaluated it on both simulated and real data and compared it to the state-of-the-art nanopore raw signal mapper Uncalled. Our results show that Sigmap yields comparable performance on mapping yeast simulated raw signals, and better mapping accuracy on mapping yeast real raw signals with a 4.4× speedup. Moreover, our method performed well on mapping raw signals to genomes of size >100 Mbp and correctly mapped 11.49% more real raw signals of green algae, which leads to a significantly higher F1-score (0.9354 versus 0.8660). AVAILABILITY AND IMPLEMENTATION: Sigmap code is accessible at https://github.com/haowenz/sigmap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new comprehensive grading for giant pituitary adenomas: SLAP grading.

AimGiant pituitary adenomas are difficult to resect due to multicompartmental extension. We developed a new grading system for giant pituitary adenomas (GPAs) considering possible extension in superior, lateral, anterior, and posterior (SLAP) directions. We also related the degree of resection to the SLAP grading.MethodsA review of case files and radiological images of patients with the GPAs defined as pituitary adenomas with a size of more than 4 cm in any dimension was done. The extent of the tumour was noted and scored as per the SLAP system. The maximum total score is 10 and represents a large tumour with maximum extensions in all directions. The subtotal resection (STR) was defined as a residual tumour volume of more than 10%. The association between individual and total score on the degree of resection was determined.ResultsA total of 103 cases of GPAs were analyzed. All patients had a suprasellar (S) extension. The lateral (L) extension was seen in 97.3% of cases. The anterior (A) extension was seen in 28 (27.2%) cases. The posterior (P) extension was seen in 45 (43.7%). Forty-eight (46.6%) had a total score of 5 or more. The STR was achieved in 64 (62.2%) cases. On regression analysis, a total score of ≥5 was associated with odds of 5.02 (1.69-14.93), p-value 0.004 for STR.ConclusionThe SLAP grading is a comprehensive grading system that can be applied easily to the GPAs and gives a complete picture of the extension of the tumour.

Weighted minimizer sampling improves long read mapping.

MOTIVATION: In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g. Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions. RESULTS: We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering a weight for each k-mer; i.e. the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes. AVAILABILITY AND IMPLEMENTATION: Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap.

Horizontal Gaze Palsy, Scoliosis, and Split Pons Sign in a 6-Year-Old Girl.

ABSTRACT: A 6-year-old girl presented with complaints of absent horizontal eye movements since birth. There was also associated progressive scoliosis for past 1 year. Neuroimaging revealed split pons sign, butterfly-shaped medulla, and prominent inferior olivary nuclei. The presence of congenital horizontal gaze palsy, childhood onset progressive scoliosis, and abnormal neuroimaging findings confirmed the diagnosis of horizontal gaze palsy with progressive scoliosis. This case highlights the importance of neuroimaging in a child presenting with horizontal gaze palsy and scoliosis that helped for starting early rehabilitation of the child, prevention of permanent vision loss, and parental counseling for future pregnancies.

A comprehensive evaluation of long read error correction methods.

BACKGROUND: Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. RESULTS: In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. CONCLUSIONS: Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE .

Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software.

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

A fast adaptive algorithm for computing whole-genome homology maps.

Motivation: Whole-genome alignment is an important problem in genomics for comparing different species, mapping draft assemblies to reference genomes and identifying repeats. However, for large plant and animal genomes, this task remains compute and memory intensive. In addition, current practical methods lack any guarantee on the characteristics of output alignments, thus making them hard to tune for different application requirements. Results: We introduce an approximate algorithm for computing local alignment boundaries between long DNA sequences. Given a minimum alignment length and an identity threshold, our algorithm computes the desired alignment boundaries and identity estimates using kmer-based statistics, and maintains sufficient probabilistic guarantees on the output sensitivity. Further, to prioritize higher scoring alignment intervals, we develop a plane-sweep based filtering technique which is theoretically optimal and practically efficient. Implementation of these ideas resulted in a fast and accurate assembly-to-genome and genome-to-genome mapper. As a result, we were able to map an error-corrected whole-genome NA12878 human assembly to the hg38 human reference genome in about 1 min total execution time and <4 GB memory using eight CPU threads, achieving significant improvement in memory-usage over competing methods. Recall accuracy of computed alignment boundaries was consistently found to be >97% on multiple datasets. Finally, we performed a sensitive self-alignment of the human genome to compute all duplications of length ≥1 Kbp and ≥90% identity. The reported output achieves good recall and covers twice the number of bases than the current UCSC browser's segmental duplication annotation. Availability and implementation: https://github.com/marbl/MashMap.

Comparing Outcomes of Robotic and Open Inguinal Lymph Node Dissection in Patients with Carcinoma of the Penis.

PURPOSE: We compared outcomes between robot-assisted video endoscopic inguinal lymphadenectomy and open inguinal lymph node dissection in patients without bulky nodal metastasis in a tandem contemporary cohort. MATERIALS AND METHODS: We retrospectively analyzed a prospectively maintained hospital registry of 51 patients who underwent robot-assisted video endoscopic inguinal lymphadenectomy and 100 treated with open inguinal lymph node dissection from 2012 to 2016 for groins without bulky nodal metastasis and who had a minimum 9-month followup. Complications were graded by the Clavien-Dindo classification, and nodal yield and disease recurrence during followup were assessed. Elastic net regression was used to select variables associated with major complications (Clavien 3a or greater) for multivariable analysis of plausible factors, including patient age, diabetes, body mass index, smoking, nodal stage, surgery type, sartorius transposition, saphenous vein transection and adjuvant radiotherapy. Penalized likelihood logistic regression methods were used for multivariate analysis to ascertain final effect sizes while accounting for sparse data bias. RESULTS: Robot-assisted video endoscopic inguinal lymphadenectomy and open inguinal lymph node dissection had comparable median lymph node yields (13 vs 12.5). No patient experienced recurrence during the median followup of 40 months. Robot-assisted video endoscopic inguinal lymphadenectomy was associated with significantly lower hospital stay, days needing a drain in situ, incidence of major complications, edge necrosis, flap necrosis and severe limb edema. On multivariable analysis pathological nodal stage (OR 2.8, 95% CI 1.1-6.8, p = 0.027) and open inguinal lymph node dissection (OR 7.5, 95% CI 1.3-43, p = 0.024) emerged as independent risk factors associated with an increased risk of major complications. CONCLUSIONS: Robot-assisted video endoscopic inguinal lymphadenectomy is a feasible technique which allows for a similar nodal yield while being associated with lower morbidity than open inguinal lymph node dissection in patients without bulky groin adenopathy.

A proteomic atlas of insulin signalling reveals tissue-specific mechanisms of longevity assurance.

Lowered activity of the insulin/IGF signalling (IIS) network can ameliorate the effects of ageing in laboratory animals and, possibly, humans. Although transcriptome remodelling in long-lived IIS mutants has been extensively documented, the causal mechanisms contributing to extended lifespan, particularly in specific tissues, remain unclear. We have characterized the proteomes of four key insulin-sensitive tissues in a long-lived Drosophila IIS mutant and control, and detected 44% of the predicted proteome (6,085 proteins). Expression of ribosome-associated proteins in the fat body was reduced in the mutant, with a corresponding, tissue-specific reduction in translation. Expression of mitochondrial electron transport chain proteins in fat body was increased, leading to increased respiration, which was necessary for IIS-mediated lifespan extension, and alone sufficient to mediate it. Proteasomal subunits showed altered expression in IIS mutant gut, and gut-specific over-expression of the RPN6 proteasomal subunit, was sufficient to increase proteasomal activity and extend lifespan, whilst inhibition of proteasome activity abolished IIS-mediated longevity. Our study thus uncovered strikingly tissue-specific responses of cellular processes to lowered IIS acting in concert to ameliorate ageing.

Co-linear chaining on pangenome graphs.

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width and how incorporating gap cost in the scoring function improves alignment accuracy. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy. Implementation ( https://github.com/at-cg/PanAligner ).

Gap-Sensitive Colinear Chaining Algorithms for Acyclic Pangenome Graphs.

A pangenome graph can serve as a better reference for genomic studies because it allows a compact representation of multiple genomes within a species. Aligning sequences to a graph is critical for pangenome-based resequencing. The seed-chain-extend heuristic works by finding short exact matches between a sequence and a graph. In this heuristic, colinear chaining helps identify a good cluster of exact matches that can be combined to form an alignment. Colinear chaining algorithms have been extensively studied for aligning two sequences with various gap costs, including linear, concave, and convex cost functions. However, extending these algorithms for sequence-to-graph alignment presents significant challenges. Recently, Makinen et al. introduced a sparse dynamic programming framework that exploits the small path cover property of acyclic pangenome graphs, enabling efficient chaining. However, this framework does not consider gap costs, limiting its practical effectiveness. We address this limitation by developing novel problem formulations and provably good chaining algorithms that support a variety of gap cost functions. These functions are carefully designed to enable fast chaining algorithms whose time requirements are parameterized in terms of the size of the minimum path cover. Through an empirical evaluation, we demonstrate the superior performance of our algorithm compared with existing aligners. When mapping simulated long reads to a pangenome graph comprising 95 human haplotypes, we achieved 98.7% precision while leaving <2% of reads unmapped.