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It is now more than sixty years since Ben E. Perry published his groundbreaking studies on the ‘Sindbādnāma’ in an essay entitled ‘The Origin of the Book of Sindbad’ (1960), in which he surveyed and discussed the vast literature about the genesis and dissemination of this book. Since then, the book’s Oriental career, from its inception in pre-Islamic Iran to its amazing world journey in the form of mediaeval European translations, adaptation, transformations, or imitations, remains still at the stage where Perry left it. The Persian and Arabic versions of the book have not received the kind of attention that a comparative study of an exemplary sample of world literature of this magnitude entails. Just to narrow the gap in the division between ‘the West’ and ‘the Rest’, I propose to look briefly at the contents of Ẓahīrī of Samarqand’s Persian ‘Sindbādnāma’ (written c. 1161) not as a misogynistic text but, rather, as a ‘mirror for princes’. Keywords Sindbādnāma; Mirror for Princes; Translation Movement; Persian Political Thought, Das Mittelalter. Perspektiven mediävistischer Forschung, Bd. 28 Nr. 1 (2023): Die ‚Sieben weisen Meister‘ als globale Erzähltradition/The ‘Seven Sages of Rome’ as a Global Narrative Tradition

Additional file 1: Supplementary figures S1 ��� S11. Figures demonstrating benchmark results between the methods compared throughout the study. AGAMEMNON results using 16 publicly available RNA-Seq tissue samples from the ENCODE consortium.

Additional file 3. Demonstration of the first use-case scenario using AGAMEMNON and employing visualizations and differential abundance analyses using data from the Feng et al.

Additional file 5. Review history.

The analysis of the high throughput sequencing (HTS) data includes a number of involved computational steps, ranging from the assembly of transcriptome, mapping or alignment of the reads to existing or assembled sequences, estimating the abundance of sequenced molecules, performing differential or comparative analysis between samples, and even inferring dynamics of interest from snapshot data. Many methods have been developed for these different tasks that provide various trade-offs in terms of accuracy and speed, because accuracy and robustness typically come at the expense of sacrificing speed and vice versa. In this work, I focus on the problems of alignment and quantification of RNA-seq data, and review different aspects of the available methods for these problems. I explore finding a reasonable balance between these competing goals, and introduce methods that provide accurate results without sacrificing speed. Alignment of sequencing reads to known reference sequences is a challenging computational step in the RNA-seq pipeline mainly because of the large size of sample data and reference sequences, and highly-repetitive sequence. Recently, the concept of lightweight alignment is introduced to accelerate the mapping step of abundance estimation.I collaborated with my colleagues to explore some of the shortcomings of the lightweight alignment methods, and to address those with a new approach called the selective-alignment. Moreover, we introduce an aligner, Puffaligner, which benefits from both the indexing approach introduced by the Pufferfish index and also selective-alignment to produce accurate alignments in a short amount of time compared to other popular aligners. To improve the speed of RNA-seq quantification given a collection of alignments, some tools group fragments (reads) into equivalence classes which are sets of fragments that are compatible with the same subset of reference sequences. Summarizing the fragments into equivalence classes factorizes the likelihood function being optimized and increases the speed of the typical optimization algorithms deployed. I explore how this factorization affects the accuracy of abundance estimates, and propose a new factorization approach that demonstrates higher fidelity to the non-approximate model. Finally, estimating the posterior distribution of the transcript expressions is a crucial step in finding robust and reliable estimates of transcript abundance in the presence of high levels of multi-mapping. To assess the accuracy of their point estimates, quantification tools generate inferential replicates using techniques such as Bootstrap sampling and Gibbs sampling. The utility of inferential replicates has been portrayed in different downstream RNA-seq applications, i.e., performing differential expression analysis. I explore how sampling from both observed and unobserved data points (reads) improves the accuracy of Bootstrap sampling. I demonstrate the utility of this approach in estimating allelic expression with RNA-seq reads, where the absence of unique mapping reads to reference transcripts is a major obstacle for calculating robust estimates.

Additional file 1 Supplementary Material for “Alignment and mapping methodology influence transcript abundance estimation”. The file contains supplementary tables and figures for the manuscript.

Additional file 2 Review history.

The rapid growth in the generation of single-cell RNA-seq (scRNA-seq) data highlights the need for scalable computational platforms to extract useful information from this data, such as gene expression estimates and the corresponding uncertainty information, that can be used in downstream applications. We present a flexible time- and memory-efficient framework for processing various types of scRNA-seq data that accounts for multi-mapping sequencing reads and that estimates the quantification uncertainty inherent in the gene counts. This uncertainty arises from gene-ambiguous UMIs that are particularly problematic, as they tend not to arise randomly, but instead arise preferentially from sequence-similar gene families. Alevin, and the new extension, alevin-fry, support a principled approach for estimating this expression uncertainty using a cell-level bootstrapping procedure. Alevin-fry is a framework that processes the mapping information generated by alevin, constructs an intermediate cell-level representation of the interactions among reads, UMIs, and genes called parsimonious UMI graphs (PUGs), and exposes multiple strategies, ranging from trivial to sophisticated, for resolving PUGs into gene-level counts. We observe that alevin and alevin-fry are capable of processing tagged-end single-cell data accurately, quickly and with very low memory requirements (usually ~2GB).Alevin is written in C++14 and is available as part of salmon at https://github.com/COMBINE-lab/salmon.Alevin-fry is written in Rust and is available at https://github.com/COMBINE-lab/alevin-fry.

Editors: Emilia calvo, Mercè Comes, Roser Puig, Mònica Rius. Assistents: Theodoro Loinaz, Cristina Moreno, On the occasion of the 75th +1 anniversary of the publication of Prof. J. M. Millàs Vallicrosa’s seminal work Assaig d’història de les idees físiques i matemàtiques a la Catalunya medieval by the Institut d'Estudis Catalans, the Commission on the History of Science and Technology in Islamic Societies (International Union on History and Philosophy of Science), the Grup Millàs Vallicrosa d’Història de la Ciència Àrab (Universitat de Barcelona) and the Societat Catalana d’Història de la Ciència i de la Tècnica (Institut d’Estudis Catalans) organized a conference entitled “A Shared Legacy: Islamic Science East and West”. At this conference, the Islamic Scientific Manuscripts Initiative, a new joint project for the study of manuscripts, was presented. Although the papers published in this volume deal with a mixture of subjects and disciplines - astronomical instruments, planetary models, geometry, medicine, miqat, technology and cartography - they all have the transmission of knowledge between the two shores of the Mediterranean as a common underlying thread. A Shared Legacy: Islamic Science East and West is “probably the most important international meeting in the history of medieval science held in Barcelona since 1959” (J. Samsó in the Foreword).

Efficient pangenome indexes are promising tools for many applications, including rapid classification of nanopore sequencing reads. Recently, a compressed-index data structure called the "move structure" was proposed as an alternative to other BWT-based indexes like the FM index and r-index. The move structure uniquely achieves both O(r) space and O(1)-time queries, where r is the number of runs in the pangenome BWT. We implemented Movi, an efficient tool for building and querying move-structure pangenome indexes. While the size of the Movi's index is larger than the r-index, it scales at a smaller rate for pangenome references, as its size is exactly proportional to r, the number of runs in the BWT of the reference. Movi can compute sophisticated matching queries needed for classification - such as pseudo-matching lengths and backward search - at least ten times faster than the fastest available methods, and in some cases more than 30-fold faster. Movi achieves this speed by leveraging the move structure's strong locality of reference, incurring close to the minimum possible number of cache misses for queries against large pangenomes. We achieve still further speed improvements by using memory prefetching to attain a degree of latency hiding that would be difficult with other index structures like the r-index. Movi's fast constant-time query loop makes it well suited to real-time applications like adaptive sampling for nanopore sequencing, where decisions must be made in a small and predictable time interval.