notes_10Q.md

Williams2017:

"it is self‐evident that life science is data science. High‐throughput nucleic acid sequencing has given rise to massive amounts of data, including repositories such as the NCBI Sequence Read Archive (SRA). The SRA's 4.5 quadrillion bases of sequence are certain to be an invaluable resource for discovery. Clearly, data production is no longer a bottleneck; genome sequencing costs have decreased 1000‐fold in the last decade and are undergoing another steep decrease this year.2 Advances in image acquisition and analysis promise to accelerate on par with high‐throughput sequencing"

Rossi2018

• "Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. "
• "More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too." "Big data domains are those able to store data in the order of magnitude of Peta to Exabyte. One Exabyte equals 1 billion Gigabytes, being the Gigabyte the scale in which the current portable storage cards are measured (our smartphones work with memories of 16 Gigabytes on average). Storage volumes are actually much smaller than volumes produced by the acquisition processes, which globally sum up to the order of zettabytes (the actual footprint), due to the fact that intermediate data are often heavily pruned and selected by quality control and data reduction processes. According to the recorded historical growth rate, the growth of DNA sequencing (in number of genomes) is almost twice as fast as predicted by Moore's Law, i.e., it doubled every 7 months since the first Illumina genome sequences in 2008. Due to these numbers genomics is comparable to other big data domains, such as astronomy, physics, and social media (particularly Twitter and YouTube). Research institutions and consortia are sequencing genomes at unprecedented rhythms, collecting genomic data for thousands of individuals, such as the Genomics England project (Genomics England, 2017) or Saudi Human Genome Program (Saudi Genome Project Team, 2015)."
• "Most laboratory equipment produces bigger volumes of data than it did in the past, and data points available in a common lab pile up to quantities not amenable to traditional processing such as electronic spreadsheets. "
• "As a consequence, the many flavors of bioinformatics and computational biology skills are now a must-have in the technologically advanced research laboratories or R&D departments: companies and research institutions, as well as single laboratories, should also promote and organize computationally skilled personnel"

Bartlett2017

• "Bioinformatics has multitudinous identities, organisational alignments and disciplinary links. This variety allows bioinformaticians and bioinformatic work to contribute to much (if not most) of life science research in profound ways."
• "The power of bioinformatic work is shaped by its dependency on life science work, which combined with the black-boxed character of bioinformatic expertise further contributes to situating bioinformatics on the periphery of the life sciences. "
• " show that bioinformatic work is operating in a social, institutional, and cultural context that presents obstacles to it receiving due credit despite its increasing importance."
• "Science itself is about producing knowledge, but the day-to-day work of science is also about securing resources, crafting collaborations, earning credit, building reputations, as well as negotiating what it is that counts as ‘important’, ‘relevant’, ‘significant’, or even ‘interesting’. "
• "Alongside their methodologies, skills and expertise, biologists and computer scientists have also brought their respective research cultures – their values and priorities – into bioinformatics, creating a hybrid inter-discipline and a hybrid culture [3]. This means that not only are there cultural as well as intellectual boundaries between biologists, computer scientists, and bioinformaticians, but there are also points of friction and tension within the broad, heterogeneous field of bioinformatics itself "
• "It is not surprising, then, that those we have spoken to have reported that many view bioinformatics as a ‘service’, rather than as a scientific field in its own right. In some cases, the development of tools that are used by life scientists renders the intellectual contribution of bioinformaticians invisible, hidden in the ‘black box’"
• interdisciplinary work is risky It falls outside of established power structures, it does not fit evaluation models built for disciplinary scientific work [8] and, related to these facts, it is does not generate the same degree of respect from both peers and public, partly because the lack of a decades-long track record of accomplishments.
• we increasingly see bioinformaticians co-designing laboratory experiments and entire studies to optimise inputs, and by consequence, optimise outputs. Bioinformaticians are, without physically producing primary inscriptions, increasingly taking responsibility for them. But despite that responsibility growing, translation of these contributions into scientific credit lags well behind.

Bartlett2018:

• middling: bridging the gap between computer science and biology but as yet not forming its own, coherent, disciplinary space, nor occupying those of its ‘parental’ disciplines
• Importantly, biologists have institutional ‘ownership’ of the data of Big Data biology.
• The locus of legitimate interpretation for Big Data biology is located firmly within the epistemic, disciplinary culture of biology: data are produced within the discipline, in laboratories, by biologists, or by computer scientists with biological sensibilities in mind. That is, although computational and statistical expertise has been drawn into the discipline, bringing with it a new style of statistical reasoning (Leonelli, 2012; Lewis et al., 2016), it has been done so in a way that positions it subordinate to the disciplinary concerns of biology
• Expertise in data analysis alone is not deemed sufficient to make legitimate biological knowledge claims. Biologists, as the creators of the primary inscriptions and the holders of cultural and institutional power, are the legitimate interpreters of Big Data biology, with the computer scientists/bioinformaticians who produce the ‘secondary inscriptions’ being dependent on, and deferring to, biologists. Bioinformatics may be an offshoot of biology, but it is tied inextricably to the disciplinary culture and institutions of biology. Physics, with a long tradition of dealing with Big Data, ‘produces’ its own computer scientists, and ‘Big Data’ physics is, mostly, conducted within the disciplinary space of ‘physics’.

Lewis2016

• "Bioinformatics – the so-called shotgun marriage between biology and computer science – is an interdiscipline. Despite interdisciplinarity being seen as a virtue, for having the capacity to solve complex problems and foster innovation, it has the potential to place projects and people in anomalous categories"

Chasapi2019 The utter relevance to biomedical research and human health started emerging as a serious proposition, following the sequencing of the human genome [31].

Wang2020

• "Bartlett, Lewis, and Williams (2016), examining struggles for epistemic authority over the emerging field of bioinformatics, suggest that biologists have retained cultural power as legitimate interpreters of biological world. Conversely the important contributions of diverse computational specialists in creating protocols, developing algorithms and data curation in processing data from large scale biological experiments– might not be recognised and thus rewarded/ valorised for example when the work was published in a biological science journal."
• "Large scale bioinformatics centres are strategic sites where these new expert roles are being developed and elaborated."
• • "Though commercial gene sequencing facilities had achieved high levels of efficiency in sequencing, questions have been raised about the quality and scientific value of outputs of large-scale and in particular commercial laboratories."
• QUote from a BGI member: " it is highly possible that they are not able to achieve the goals if they do all jobs by themselves – the data, information and results could be out of their reach. Under such circumstances, computer scientists, mathematicians and others are needed to collect and process the data and information that the biologists want. I accept that the biologists are the predominant members in the research project team and researchers from other related disciplines are more supporting than deciding"

Vermeulen2016 The origin of big physics was traced back to the inter-war period when universities in California began collaborating to find a solution for the problems of power production and distribution (Galison 1992; Seidel 1992). Large-scale physics research spread internationally after the important contribution of large-scale physics research to World War II.

Tractenberg2019

• "Bioinformatics, the discipline that evolved to harness computational approaches to manage and analyze life-science data, is inherently multi-disciplinary, and those trained in it therefore need to achieve an integrated understanding of both factual and procedural aspects of its diverse component fields."
• Late/advanced Journeyman (J2) (e.g., doctorate holder), Bloom’s 5, late 6: expertly evaluate (review) and synthesize novel life-science knowledge, and integrate bioinformatics into research practice. The J2 Journeyman is independent and expert in a specific life-science area, and can select, apply and develop new methods. The J2 Journeyman formulates problems, considers the relevance of “what works” within this area to other life-science domains, so as to be an adaptable and creative scientific innovator without having to reinvent every wheel.
• prioritizes the development of independent scientific reasoning and practice; it can therefore contribute to the cultivation of a next generation of bioinformaticians who are able to design rigorous, reproducible research, and critically analyze their and others’ work.

Feenstra2018

• "ability to translate research problems from one discipline to another, and communication with peers from different scientific backgrounds are key components. "
• Chang (2015) analysed a year and a half of projects in their bioinformatics core facility, and found that 46 data analysis projects had required over 34 different types of analysis methods. The vast majority of projects thus required unique, one-off approaches that were tailor-made for the task at hand, had not been used before, and likely would not be used again. In other words, there is no routine, and each analysis project becomes a research project in itself, requiring staff at PhD level to perform effectively. Note, that the lack of generalizability of such methods may be another hurdle to publication for these researchers. It should be emphasized that translation, here between project requirement and method capability, seems to have been a key element of success.
• We therefore expect that computational biologists, computer scientists and biologists—while learning and appreciating more about each other’s fields, and integrating more of each other’s approaches, methods and techniques into their own work—may likely continue their current course of intensive collaborations for some time to come
• challenging research disciplines, which require practitioners to have a well-developed concept of the art of doing science, a high ability for mathematical and algorithmic abstraction, a broadly developed knowledge (balance), an ability to quickly absorb and integrate novel concepts (translate) and well-developed modelling, engineering and practical skills (focus). These aspects are all emphasized as critically important for the job market in the life sciences (Greene et al., 2016; Via et al., 2013; Welch et al., 2014), the data sciences (Dunn and Bourne, 2017; Lyon and Mattern, 2017; Pournaras, 2017; Seidl et al., 2018) and both (Brazas et al., 2017a; Greene et al., 2016).

Smith2015

• Vincent and Charette (2015) make some excellent and compelling points in their article, and although I disagree with some of them, one of their final points resonated with me: “A good definition of a bioinformatician should not be based on a single concept …real bioinformaticians share a number of common characteristics …none of which [are] essential.” Perhaps a common characteristics that we share as bioinformaticians (and maybe this one should be essential) is a passion for using computers to understand the bewildering biological world that surrounds and encompasses us.

Smith2018 " At its surface, bioinformatics is a relevant, relatable, and stimulating discipline. At its heart, however, it is a dry, dense, and challenging topic to teach. If I describe the capabilities of user‐friendly software like Geneious, the students are interested and engaged. “This is awesome, Professor Smith! I didn't realize that I could explore all of these cool genomes right from my laptop without leaving the couch and with only rudimentary computer skills.” Bring up the finer points of the De Bruijn graph assembly method or Bayesian phylogenetics and half the class is heading for the door; and even some good old bioinformatics humor—“I hope everyone is having a BLASTX”—would not bring them back."