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Data Production And Analysis In Population Genomics Pdf

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PyPop Python for Population Genomics is an environment developed by the Thomson lab for doing large-scale population genetic analyses including: 1 conformity to Hardy-Weinberg expectations, 2 tests for balancing or directional selection; 3 estimates of haplotype frequencies and their distributions and measures and tests of significance for linkage disequilibrium LD. It is an object-oriented framework implemented in Python , a language with powerful features for interfacing with other languages, such as C in which we have already implemented many routines and which is particularly suited to computationally intensive tasks.

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Metrics details. Most of these data are publically available as unassembled short-read sequence files that require extensive processing before they can be used for analysis. The provision of data in a uniform format, which can be easily assessed for quality, linked to provenance and phenotype and used for analysis, is therefore necessary. The performance of de novo short-read assembly followed by automatic annotation using the pubMLST. Genealogical relationships compatible with, but at a higher resolution than, those identified by multilocus sequence typing were obtained with core genome comparisons and ribosomal protein gene analysis which revealed a genomic structure for a number of previously described phenotypes.

This unified system for cataloguing Neisseria genetic variation in the genome was implemented and used for multiple analyses and the data are publically available in the PubMLST Neisseria database.

The de novo assembly, combined with automated gene-by-gene annotation, generates high quality draft genomes in which the majority of protein-encoding genes are present with high accuracy.

The approach catalogues diversity efficiently, permits analyses of a single genome or multiple genome comparisons, and is a practical approach to interpreting WGS data for large bacterial population samples. The method generates novel insights into the biology of the meningococcus and improves our understanding of the whole population structure, not just disease causing lineages. These data represent a major resource for studies of bacterial diversity, evolution and function; however, as the throughput of genome finishing and annotation technologies has not kept pace with sequence determination, the genomes have to be reassembled to be interpreted.

Typically, this is done either by mapping to a reference sequence or by de novo assembly to generate draft genomes comprising multiple contiguous sequences contigs. The approach of mapping short-read sequences to a reference sequence has been effectively used to analyse WGS data from closely related isolates in numerous studies [ 2 — 9 ], especially by using the data obtained to reconstruct genealogies based on phylogenetic trees.

This approach has a number of limitations, including: the necessity for a high-quality reference sequence with which to make the comparison; variation in sequence not present in the reference cannot be detected; the approach is poorly scalable; analyses typically have to be re-run as new genomes are obtained; and finally, the density of sequence polymorphisms in the majority of bacterial populations is such that this approach is not feasible for the study of isolates that are not genetically closely related.

The use of de novo assembly methods represents an alternative, more broadly applicable approach, with assemblers based on de Brujin graphing being widely used as they deal effectively with large volumes of data [ 10 , 11 ] and can assemble short-read sequences of fewer than bases in length into contigs that contain the majority of the genome. Once they have been assembled, these sequences can be annotated by comparisons to known genes or genome databases [ 15 ], using an approach similar to that used in multilocus sequence typing MLST , which has been widely employed for sequence-based analyses at the population scale since [ 16 ].

Neisseria meningitidis , the meningococcus, is a pathogen of global significance and an informative model organism for investigating the relationship between genotype and phenotype, as it is highly diverse phenotypically and genotypically [ 18 ]. Only a very small number of infections result in devastating and rapidly progressing disease, in the form of septicaemia, meningitis, or both. For reasons that are incompletely understood, some meningococcal genotypes are much more likely to cause invasive disease than others.

There are a number of factors known to contribute to the hyperinvasive phenotype, particularly the possession of certain capsular polysaccharides, but species-level comparisons suggest that the majority of the pan-genome is widely shared among invasive and non-invasive genotypes.

This has led to the conclusion that the ability to cause invasive disease is both polygenic and different among hyperinvasive lineages [ 22 — 24 ], but the determinants associated with particular lineages remain poorly defined. Comparative WGS of meningococcal isolate collections that include representative disease and carriage isolates have the potential to define the genetic differences which determine the hyper invasive phenotypes. The draft genomes were analysed for accuracy and coverage using the BIGSdb platform [ 17 ] which enabled comparison with 24 antigen and MLST typing loci previously characterised with Sanger sequencing and four finished reference genomes, cross-validating these technologies.

These data established the robustness and reliability of using de novo draft genomes for a population-wide level of analysis for meningococcus genomes and presented a WGS description of the major hyperinvasive lineages, providing insights into their structure, evolution, and function.

Short-read sequences were assembled into draft genomes using Velvet [ 25 ] and VelvetOptimiser [ 26 ] programs, using 54 or 76 base read files. Assemblies consisted of to contigs, with a mean of This statistic provided an indication of the total genome coverage; however, it was not a measure of genome assembly quality. All the de novo assemblies consisted of contigs terminating at repetitive sequence regions longer than the read length of 54 or 76 bases and these termination regions contained a higher read depth than the preceding regions.

There were thirty-four sequence discrepancies 1. The number and distribution of sequence changes found in the resequencing experiments enabled the likely reasons for the discrepancies to be identified. In the majority of cases these could be attributed to either editing or labelling problems in the original Sanger sequencing experiments with only four instances that were a direct consequence of the assembly of the short-read sequences by the Velvet algorithm. The four MLST profiles affected by trace file editing errors maintained their original clonal complex assignment; however, their sequence type ST was amended to a new designation as a consequence of this work.

In summary, the errors in the original Sanger experiments were due to: eleven trace file editing errors; 19 samples mislabelled during Sanger sequencing; and four occurrences of Velvet mis-assembly caused by short tandem repeat STR regions.

Location of eMLST and antigen genes within the meningococcal genome. CGView map of the Neisseria meningitidis reference genome, FAM18, showing the placement of the conventional and extended MLST loci and the 3 antigen genes 4 typing fragments used to assess sequence accuracy of the de novo high-throughput assembly method across the genome. Sequence discrepancies were found between all four resequenced draft genomes and their respective finished reference genome. FAM18 and Z reference genomes, obtained using ABI and a combination of ABI and respectively, had sequence discrepancies among twenty-two annotated CDS, ten pseudogenes, five putative protein sequences and fourteen hypothetical proteins; totalling 51 loci of the published CDS sequences for these genomes.

The majority of these CDS affected The differences were categorized as non-synonymous or synonymous amino acid changes see Additional file 3 : Table S3. Paralogous gene cross-identification occurred most often in CDS annotated as hypothetical proteins, a total of ten.

These, plus six additional paralogous loci, were manually curated and defined using up- and down-stream sequence in order to enable the BIGSdb scanning function to correctly distinguish the divergent regions of the paralogous genes without manual curation.

A list containing the identification of all CDS with sequence differences, and those loci missing in the draft genomes was generated see Additional file 2 : Table S2, section A-E. The Z draft genome contained of the Comparison of the FAM18 draft to the reference genome identified of The nine CDS 0. All four resequenced genomes were also mapped to their respective finished genomes to look for the missing loci.

While it is possible to revise the assembly parameters and recover some of the missing data in the assemblies, this would potentially be at a cost to the overall quality of the assembly by swapping specificity and sensitivity and could in fact reduce the N50 value, therefore this option was not implemented for this analysis.

Technically, the foundations resulting in the underrepresentation of these regions in the subsequent sequence reads have many sources: for example GC bias affects the stability of the DNA strand which could influence the read ability or modify the probability of a fragmentation.

It has been shown that optimized or PCR-free protocols reduce GC bias affects [ 32 — 35 ] and if these genomes were resequenced using a PCR-free approach it is possible the overall genome coverage would increase. All of the draft genome assemblies were annotated using a gene-by-gene approach using the BIGSdb platform as described previously [ 17 , 36 ].

The genome data were subsequently rescanned to assign the new alleles to the respective genome in which it was found. Partially assembled loci, those found at the end of a contig, were tagged as present in the genome but flagged as incomplete.

Comparison of the four finished genomes identified CDS The list was refined by determining gene presence of the CDS in two additional finished genomes [ 37 ]. Those with an assigned EC indicated the presence of two environmental processing pathways, four genetic information processing pathways and 12 metabolic functional pathways. KEGG functional and informational processing pathways identified in the meningococcal genome.

Loci from the core gene list were used to search the pathway database KEGG for functional and informational pathways. A total loci of the core genes have assigned Enzyme Commission numbers EC. The relationships between meningococcal isolates are represented by two datasets in which a. In both trees major phylogenetic groups are noted A-D. Capsular types other than A, B, or C are noted in parentheses, accept for Lineage 11 which are labelled cps B and cps C.

This lineage sub-structure is captured in MLST by the designation of two central genotypes that are differentially associated with invasive disease, and at the sequence type level share five of the seven MLST alleles [ 27 , 41 ]. Analysis of this lineage also showed that isolates associated with the ST belonged to a well-defined monophyletic lineage, while the ST associated isolates were a more diverse but distinct lineage.

Further exploration of this complex is necessary to more fully define the relationships within this clade and the variable pathogenic nature associated with each group. The association of capsule loci with the lineage 11 ST complex , and in lineage 8 ST-8 complex , at the cgMLST level core genes shows the serotype B and C associated genomes on different branches, and only lineage 11 ST complex maintains this separation at the rMLST level 53 ribosomal genes.

The remaining lineages did not have sufficient numbers to clearly differentiate capsule associations; and additional studies with larger strain collections will be required to make these associations more distinctly. Four sets of lineage specific draft genomes, thirty-four in total, were assessed for genome coverage using one of four reference genome annotations and the BIGSdb Genome Comparator tool.

There was an average of CDS Seven isolates belonging to lineage 8 ST-8 complex were compared to the G reference genome. A further ten isolates belonged to lineage 11 ST complex and were compared to the FAM18 genome sequence. The comparison identified Ten isolates also belonged to the lineage 4 ST-4 complex and were compared to the Z genome, identifying Exhaustive comparison of bacterial genomes, including all sources of genetic variation i.

The majority, if not all, of short-read WGS data generated to date with NGS technology are incapable of meeting this ideal without extensive additional data combined with manual assembly and curation [ 42 , 43 ]. For such analyses to be robustly conducted, however, it is necessary to establish an analysis paradigm that interprets data consistently within known parameters of completeness and accuracy [ 45 ].

Here we demonstrate how bioinformatics tools that are freely available and widely understood can be combined to interrogate NGS data using the example of the diverse human pathogen Neisseria meningitidis [ 17 ]. Indeed, where comparable data were available for genes previously used for sequence based-typing, the majority of the discrepancies were due to errors in the editing or labelling of the specimens used in the original Sanger sequences, and the remaining, the result of STR sequence compression during assembly [ 49 ].

Once these errors had been taken in to account, the two approaches were in complete agreement. There was also very good agreement with complete reference genomes, although this depended on the read length of the short-read sequence data, with substantial improvement as read length increased. Data quality was also determined by the details of the chemistry and procedures used [ 51 , 52 ], showing that NGS data are optimally useful when this information is deposited with them.

Some coverage effects were seen, with sequences near the origin of replication consistently sequenced to a higher depth [ 53 ], than others but the genome of each assembly was adequately covered.

The BIGSdb platform accommodates sequence data derived from a particular isolate ranging from a single gene through multiple genes and contigs up to and including complete genomes [ 17 ].

The Genome Comparator tool can either use the annotations from a reference genome, which were used to compare the reference genomes with the assembled genomes, or sets of loci defined in the PubMLST sequence definition database, for which it maintains a complete catalogue of diversity described to date [ 15 ]. Additional loci that represent gene fragments used in typing schemes and peptide loci representing typing antigen variable regions [ 31 , 55 ], are also indexed within the database.

The currently identified paralogous loci require additional manual annotation, they have been found to vary between the Neisseria species and may vary among meningococcal lineages.

In conclusion, the approach can be used to analyse large numbers of WGS datasets consistently and is generally applicable for use across the bacterial domain. Because every bacterial isolate is potentially an unrepresentative mutant and due to the imperfect nature of NGS assemblies, the core genome cannot be simply defined as the genes present in all isolates; however, the estimate of a core genome comprising genes generated here is in good agreement with other estimates which were based on substantially fewer genomes [ 23 , 37 , 58 , 59 ].

While the membership of the core genome will be refined over time, it is unlikely to be very different from that proposed here. An updated list of meningococcal core genes will be maintained in the database. The ribosomal genes rMLST and core genome cgMLST data provide more resolution, demonstrating that the six major hyperinvasive lineages included in this dataset cluster in to a number of larger groups [ 62 ].

Some lineages are more closely related to each other although the star phylogeny demonstrates a highly diverse and recombining population from which invasive lineages have emerged independently on several occasions [ 63 ].

These data confirm that the invasive lineages are defined by sequence variation in the core genome, although certain members of the accessory genome, for example the capsule [ 66 ], the meningococcal disease associated island phage [ 67 , 68 ], and restriction modification systems [ 37 , 69 ] are differentially distributed among lineages. This nomenclature also allows for the designations of sub-lineages which our data set, and others not described here, define additional prevalent biological and phenotypic associations such as the ET mutants of lineage The proposal was presented to a satellite sub-group meeting of the XIX International Pathogenic Neisseria Conference in October , which included submitters, curators and users and the proposal is under consideration for adoption by the PubMLST Management Committee.

WGS data has the potential to unify studies of bacteria by providing comprehensive descriptions of genomic variation. To achieve this it is necessary to: i make the data available in a comprehensible way, along with information describing its completeness and accuracy; and ii link them to provenance and phenotype information, which describes the source of the sample and its properties, as well as the known properties of the genes identified and the deduced product.

These datasets will grow in completeness and accuracy over time; however, it is also necessary for these data to be presented in a stable context, enabling even incomplete information to be explored. The approach described and validated here for the meningococcus is one way of achieving this, which employs generic, freely accessible and widely used tools. The use of the web interface within the PubMLST Neisseria database enables a process of community annotation whereby different members of the community can participate in the maintenance and improvement of sequence annotation and interpretation.

Genomic DNA from diverse Neisseria meningitidis isolates was prepared from archive stocks which have been extensively characterized and previously reported [ 16 , 27 , 30 , 70 ]; this data set includes the MLST global reference collection isolates and FAM18 [ 16 , 47 ].

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The increasing availability and complexity of next-generation sequencing NGS data sets make ongoing training an essential component of conservation and population genetics research. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic e. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists. Informing conservation efforts is one of the most important and challenging needs of the genomic era Allendorf ; Lewin et al.

Metrics details. Most of these data are publically available as unassembled short-read sequence files that require extensive processing before they can be used for analysis. The provision of data in a uniform format, which can be easily assessed for quality, linked to provenance and phenotype and used for analysis, is therefore necessary. The performance of de novo short-read assembly followed by automatic annotation using the pubMLST. Genealogical relationships compatible with, but at a higher resolution than, those identified by multilocus sequence typing were obtained with core genome comparisons and ribosomal protein gene analysis which revealed a genomic structure for a number of previously described phenotypes.

It seems that you're in Germany. We have a dedicated site for Germany. Population genomics is a recently emerged discipline, which aims at understanding how evolutionary processes influence genetic variation across genomes. Today, in the era of cheaper next-generation sequencing, it is no longer as daunting to obtain whole genome data for any species of interest and population genomics is now conceivable in a wide range of fields, from medicine and pharmacology to ecology and evolutionary biology. Therefore, Data Production and Analysis in Population Genomics purposely puts emphasis on protocols and methods that are applicable to species where genomic resources are still scarce.

Computer programs for population genetics data analysis: a survival guide

Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. Genomics is a forum for describing the development of genome-scale technologies and their application to all areas of biological investigation.

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Data Production and Analysis in Population Genomics

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