Teacher agency and professional learning communities; what can Learning Rounds in Scotland teach us?

Recently there has been growth in researching teacher agency. Some research has considered the relationship between teacher agency and professional learning. Similarly, there has been growing interest in professional learning communities as resources for professional learning. Connections have been made between professional learning communities and teacher agency, with professional learning communities seen as an affordance for the exercise of teacher agency. However, it has also been argued that there is little detailed evidence of what happens inside professional learning communities or of teacher agency in action. The research reported here focuses on a form of professional learning community from Scotland: Learning Rounds. It uses data from transcripts of post classroom observation conversations to consider the extent to which Learning Rounds provide an affordance for teacher agency and the extent to which that affordance is utilised. This research makes a contribution in three ways: adding to an empirical understanding of what happens in professional learning communities; understanding how teacher agency is (or is not) exercised in practice; considering what factors might affect the utilisation (or otherwise) of affordances for teacher agency. The paper concludes with several recommendations for developing effective professional learning communities as an affordance for teacher agency

From Indexing Data Structures to de Bruijn Graphs

International audienceNew technologies have tremendously increased sequencing throughput com-pared to traditional techniques, thereby complicating DNA assembly. Hence, as-sembly programs resort to de Bruijn graphs (dBG) of k-mers of short reads to compute a set of long contigs, each being a putative segment of the sequenced molecule. Other types of DNA sequence analysis, as well as preprocessing of the reads for assembly, use classical data structures to index all substrings of the reads. It is thus interesting to exhibit algorithms that directly build a dBG of order k from a pre-existing index, and especially a contracted version of the dBG, where non branching paths are condensed into single nodes. Here, we formalise the relation-ship between suffix trees/arrays and dBGs, and exhibit linear time algorithms for constructing the full or contracted dBGs. Finally, we provide hints explaining why this bridge between indexes and dBGs enables to dynamically update the order k of the graph

From Indexing Data Structures to de Bruijn Graphs

International audienceNew technologies have tremendously increased sequencing throughput com-pared to traditional techniques, thereby complicating DNA assembly. Hence, as-sembly programs resort to de Bruijn graphs (dBG) of k-mers of short reads to compute a set of long contigs, each being a putative segment of the sequenced molecule. Other types of DNA sequence analysis, as well as preprocessing of the reads for assembly, use classical data structures to index all substrings of the reads. It is thus interesting to exhibit algorithms that directly build a dBG of order k from a pre-existing index, and especially a contracted version of the dBG, where non branching paths are condensed into single nodes. Here, we formalise the relation-ship between suffix trees/arrays and dBGs, and exhibit linear time algorithms for constructing the full or contracted dBGs. Finally, we provide hints explaining why this bridge between indexes and dBGs enables to dynamically update the order k of the graph

On the Representation of de Bruijn Graphs

The de Bruijn graph plays an important role in bioinformatics, especially in the context of de novo assembly. However, the representation of the de Bruijn graph in memory is a computational bottleneck for many assemblers. Recent papers proposed a navigational data structure approach in order to improve memory usage. We prove several theoretical space lower bounds to show the limitation of these types of approaches. We further design and implement a general data structure (DBGFM) and demonstrate its use on a human whole-genome dataset, achieving space usage of 1.5 GB and a 46% improvement over previous approaches. As part of DBGFM, we develop the notion of frequency-based minimizers and show how it can be used to enumerate all maximal simple paths of the de Bruijn graph using only 43 MB of memory. Finally, we demonstrate that our approach can be integrated into an existing assembler by modifying the ABySS software to use DBGFM.Comment: Journal version (JCB). A preliminary version of this article was published in the proceedings of RECOMB 201