Update citations (#1289)

Co-authored-by: bgruening <[email protected]>

_bibliography/citations-eu.bib

@@ -6936,6 +6936,22 @@ @article{manna_endosomal_2023
  year = {2023}
 }
 
+@article{marco_dorq-seq_2024,
+ abstract = {Due to its high modification content tRNAs are notoriously hard to quantify by reverse transcription and RNAseq. Bypassing numerous biases resulting from concatenation of enzymatic treatments, we here report a hybrid approach that harnesses the advantages of hybridization-based and deep sequencing–based approaches. The method renders obsolete any RNAseq related workarounds and correction factors that affect accuracy, sensitivity, and turnaround time. Rather than by reverse transcription, quantitative information on the isoacceptor composition of a tRNA pool is transferred to a cDNA mixture in a single step procedure, thereby omitting all enzymatic conversations except for the subsequent barcoding PCR. As a result, a detailed tRNA composition matrix can be obtained from femtomolar amounts of total tRNA. The method is fast, low in cost, and its bioinformatic data workup surprisingly simple. These properties make the approach amenable to high-throughput investigations including clinical samples, as we have demonstrated by application to a collection of variegated biological questions, each answered with novel findings. These include tRNA pool quantification of polysome-bound tRNA, of tRNA modification knockout strains under stress conditions, and of Alzheimer patients’ brain tissues.},
+ author = {Marco, Kristen and Marc, Lander and Lea-Marie, Kilz and Lukas, Gleue and Marko, Jörg and Damien, Bregeon and Djemel, Hamdane and Virginie, Marchand and Yuri, Motorin and Kristina, Friedland and Mark, Helm},
+ doi = {10.1093/nar/gkae765},
+ issn = {0305-1048},
+ journal = {Nucleic Acids Research},
+ keywords = {{\textgreater}UseGalaxy.eu},
+ month = {September},
+ pages = {gkae765},
+ shorttitle = {{DORQ}-seq},
+ title = {{DORQ}-seq: high-throughput quantification of femtomol {tRNA} pools by combination of {cDNA} hybridization and {Deep} sequencing},
+ url = {https://doi.org/10.1093/nar/gkae765},
+ urldate = {2024-09-14},
+ year = {2024}
+}
+
 @article{marisaldi_novo_2021,
  abstract = {In the present work, we assembled and characterized a de novo larval transcriptome of the Atlantic bluefin tuna Thunnus thynnus by taking advantage of publicly available databases with the goal of better understanding its larval development. The assembled transcriptome comprised 37,117 protein-coding transcripts, of which 13,633 full-length ({\textgreater}80\% coverage), with an Ex90N50 of 3061 bp and 76\% of complete and single-copy core vertebrate genes orthologues. Of these transcripts, 34,980 had a hit against the EggNOG database and 14,983 with the KEGG database. Codon usage bias was identified in processes such as translation and muscle development. By comparing our data with a set of representative fish species, 87.1\% of tuna transcripts were included in orthogroups with other species and 5.1\% in assembly-specific orthogroups, which were enriched in terms related to muscle and bone development, visual system and ion transport. Following this comparative approach, protein families related to myosin, extracellular matrix and immune system resulted significantly expanded in the Atlantic bluefin tuna. Altogether, these results provide a glimpse of how the Atlantic bluefin tuna might have achieved early physical advantages over competing species in the pelagic environment. The information generated lays the foundation for future research on the more detailed exploration of physiological responses at the molecular level in different larval stages and paves the way to evolutionary studies on the Atlantic bluefin tuna.},
  author = {Marisaldi, Luca and Basili, Danilo and Gioacchini, Giorgia and Canapa, Adriana and Carnevali, Oliana},